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1

Clinical Study Report

HO-16-16611; eTrack: 206347

A propensity score-matched study of systemic

lupus erythematosus related organ damage in

the BLISS long term extension trials (BEL112233

and BEL112234) and the Toronto Lupus Cohort

Prepared for: GlaxoSmithKline (GSK)

By: Medical Decision Modeling Inc. (MDM)

January 10, 2018

2

Table of Contents

EXECUTIVE SUMMARY ..........................................................................................................15

1 INTRODUCTION ................................................................................................................17

2 OBJECTIVES .....................................................................................................................18

2.1 Primary Objective ............................................................................................................18

2.2 Secondary Objectives......................................................................................................18

2.3 Exploratory Objectives .....................................................................................................18

3 SELECTION OF THE EXTERNAL SLE COHORT ..............................................................18

4 POPULATION ....................................................................................................................19

5 METHODS .........................................................................................................................21

5.1 Choice of Propensity Score Matching Variables ..............................................................21

5.2 Propensity Score Matching Methods ...............................................................................26

5.3 Assessment of Post PSM Covariate Balance ..................................................................27

5.4 Visit Selection ..................................................................................................................27

5.5 Primary and Secondary Endpoints ..................................................................................28

5.5.1 Primary endpoint: difference in change of SDI from baseline to 5 years ....................28

5.5.2 Difference in time to first SDI worsening ...................................................................28

5.5.3 Change from baseline SDI score by year interval ......................................................28

5.5.4 Difference of change from baseline SDI by year interval ...........................................30

5.5.5 Transition analysis of SDI from baseline over a 5-year interval .................................30

5.5.6 Change from baseline of SDI organ damage system subscores ...............................30

5.5.7 Frequency of increase from baseline of SDI organ damage system subscores .........31

5.5.8 Difference in mean SLEDAI score from baseline over a 5-year interval ....................31

5.5.9 Difference in cumulative corticosteroid usage from baseline over a 5-year interval ...32

5.6 Exploratory Endpoints .....................................................................................................32

5.6.1 Difference in change of SDI from baseline to 5 years ................................................32








5.6.9 Difference in cumulative corticosteroid usage from baseline over a 5-year interval ...35

3

5.7 Diagnostic Analyses ........................................................................................................35

5.7.1 Baseline characteristics of unmatched study arms ....................................................36

5.7.1.1 BEL112233 LTE and TLC ...................................................................................36

5.7.1.2 Pooled LTE and TLC ..........................................................................................36

5.7.1.3 Comparison methods..........................................................................................36

5.7.2 Baseline characteristics of matched samples ............................................................36

5.7.2.1 BEL112233 LTE and TLC ...................................................................................36



5.7.3 Distribution of year 5 data point timing ......................................................................37

5.7.3.1 US BLISS LTE and TLC .....................................................................................37

5.7.3.2 Pooled BLISS LTE and TLC ...............................................................................37


5.7.4 Patients withdrawing from LTE and TLC cohorts .......................................................37

5.7.4.1 BEL112233 LTE and TLC ...................................................................................37



5.7.5 BLISS LTE subjects randomized to SoC in parent trial..............................................38

5.7.5.1 BEL112233 LTE and TLC ...................................................................................38


5.7.5.3 Comparison Methods..........................................................................................38

5.7.6 Belimumab baseline of BLISS LTE subjects .............................................................39

5.7.6.1 BEL112233 LTE and TLC ...................................................................................39



6 RESULTS ...........................................................................................................................39

6.1 Primary and Secondary Analyses ....................................................................................39

6.1.1 Propensity score matching ........................................................................................39

6.1.1.1 BEL112233 and TLC Patients with 5-years follow up .........................................39

6.1.1.2 BEL112233 and TLC patients with ≥ 1-year follow up for time to event analyses 43

6.1.2 Primary endpoint – Difference in change in SDI from baseline to 5 years .................47





4




6.1.10 Difference in cumulative corticosteroid usage from baseline over a 5-year interval 82

6.2 Exploratory Analyses .......................................................................................................84

6.2.1 Propensity score matching ........................................................................................84

6.2.1.1 Pooled LTE and TLC Patients with 5-years follow up .........................................84

6.2.1.2 Pooled LTE and TLC Patients with ≥ 1-year follow up for time to event analyses

88

6.2.2 Difference in change in SDI from baseline to 5 years ................................................92



6.2.5 Difference of change from baseline SDI by year interval ......................................... 102

6.2.6 Transition analysis of SDI from baseline over a 5-year interval ............................... 107

6.2.7 Change from baseline of SDI organ damage system subscores ............................. 108

6.2.8 Frequency of increase from baseline of SDI organ damage system subscores ....... 119

6.2.9 Difference in mean SLEDAI score from baseline over a 5-year interval .................. 129

6.2.10 Difference in cumulative corticosteroid usage from baseline over a 5-year interval

129

6.3 Diagnostics .................................................................................................................... 130

6.3.1 Baseline characteristics of unmatched study arms .................................................. 130

6.3.1.1 BEL112233 LTE and TLC ................................................................................. 130

6.3.1.2 Pooled LTE and TLC ........................................................................................ 130

6.3.2 Baseline characteristics of matched samples .......................................................... 130

6.3.2.1 BEL112233 LTE and TLC ................................................................................. 130

6.3.2.2 Pooled LTE and TLC ........................................................................................ 131

6.3.3 Distribution of year 5 data point timing .................................................................... 131

6.3.3.1 BEL112233 LTE and TLC ................................................................................. 131

6.3.3.2 Pooled BLISS and TLC ..................................................................................... 133

6.3.4 Patients withdrawing from US LTE, Pooled LTE and TLC cohorts .......................... 134

6.3.5 BLISS US LTE and pooled LTE subjects randomized to SoC ................................. 142

6.3.6 Belimumab baseline of US LTE and pooled subjects .............................................. 143

7 DISCUSSION AND CONCLUSIONS ................................................................................ 146

7.1 Discussion ..................................................................................................................... 146

7.2 Conclusions ................................................................................................................... 147

5

8 REFERENCES ................................................................................................................. 149

6

List of Figures

Figure 1. Common support in full model with all patients (n=567) .............................................41

Figure 2. Common support in full model with all patients (N=965) .............................................45

Figure 3. Common support in full model with all patients (n=973) .............................................86

Figure 4. Common support in full model with all patients (N=1541) ...........................................90

Figure 5. BEL112233 and TLC 5th year distributions of years from baseline............................ 132

Figure 6. Pooled BLISS and TLC 5th year distributions of years from baseline ........................ 133

7

List of Tables

Table 1. Comparable instruments used for the BEL112233 LTE and TLC ................................19

Table 2. Eligibility criteria. .........................................................................................................20

Table 3. Raw sample sizes in BEL112233, BEL112234 and the TLC .......................................20

Table 4. Sample sizes in BEL112233, BEL112234 and the TLC for 5 year analyses and time to

event analyses. .................................................................................................................21

Table 5. Predictors of organ damage found in the literature ......................................................22

Table 6. Propensity score baseline matching variables in the data and how they were

operationalized. .................................................................................................................23

Table 7. Definitions of variables ................................................................................................24

Table 8. Results of full propensity score logistic regression model, BEL112233 and TLC dataset

with 5 years follow-up (N=567) ..........................................................................................40

Table 9. Summary statistics of PSM variable, BEL112233 and TLC dataset with 5 years follow-

up (N=567) ........................................................................................................................40

Table 10. Bias prior to propensity score matching, BEL112233 and TLC dataset with 5 years

follow-up (N=567) ..............................................................................................................42

Table 11. Bias post PS matching, BEL112233 and TLC dataset with 5 years follow-up (n=198)

..........................................................................................................................................43

Table 12. Results of full propensity score logistic regression model, BEL112233 and TLC

dataset with ≥ 1 year follow-up (N=965) ............................................................................44

Table 13. Summary statistics of PSM variable, BEL112233 and TLC dataset with ≥ 1 year

follow-up (N=965) ..............................................................................................................44

Table 14. Bias prior to PS matching, BEL112233 and TLC dataset with ≥ 1 year follow-up

(N=965) .............................................................................................................................46

Table 15. Bias post PS matching, BEL112233 and TLC dataset with ≥ 1 year follow-up (N=358)

..........................................................................................................................................47

Table 16. Year 5 Total SDI difference of change from baseline .................................................48

Table 17. Year 5 Total SDI difference of change from baseline using inverse propensity score

weighting (N=567) .............................................................................................................48

Table 18. Bias using inverse propensity score weighting (N=567) .............................................49

Table 19. Year 5 Total SDI difference of change from baseline using regression augmented

IPSW (N=567) ...................................................................................................................49

Table 20. Year 5 Total SDI difference of change from baseline all methods (N=567) ................50

8

Table 21. Proportional hazards model of time to first change in total SDI score, exponential

distribution .........................................................................................................................50

Table 22. Proportional hazards model of time to first change in total SDI score, Gompertz

distribution. ........................................................................................................................51

Table 23. Proportional hazards model of time to first change in total SDI score, Weibull

distribution. ........................................................................................................................51

Table 24. Accelerated failure time model of time to first change in total SDI score, loglogistic

distribution .........................................................................................................................51

Table 25. Accelerated failure time model of time to first change in total SDI score, lognormal

distribution .........................................................................................................................52

Table 26. Fit of regression models of time to first change in total SDI score ..............................52

Table 27. SDI change from baseline .........................................................................................53

Table 28. SDI change from baseline constrained model year 1 .................................................54





Table 33. Year 1 Total SDI difference of change from baseline controlled for entry decade ......59






Table 39. Annual transition probabilities ....................................................................................62

Table 40. SDI ocular system subscore change from baseline by year ......................................62

Table 41. Fisher’s test for belimumab versus SoC SDI ocular subscore change from baseline .63

Table 42. SDI neuropsychiatric system subscore change from baseline by year .......................63

Table 43. Fisher’s test for belimumab versus SoC SDI neuropsychiatric system subscore

change from baseline ........................................................................................................64

Table 44. SDI renal system subscore change from baseline by year ........................................64

Table 45. Fisher’s test for belimumab versus SoC SDI renal system subscore change from

baseline .............................................................................................................................65

Table 46. SDI pulmonary system subscore change from baseline by year ................................65

Table 47. Fisher’s test for belimumab versus SoC SDI pulmonary system subscore change from

baseline .............................................................................................................................66

9

Table 48. SDI cardiovascular system subscore change from baseline by year ..........................66

Table 49. Fisher’s test for belimumab versus SoC SDI CV subscore change from baseline .....67

Table 50. SDI peripheral vascular system subscore change from baseline by year ..................67

Table 51. Fisher’s test for belimumab versus SoC SDI peripheral vascular system subscore


Table 52. SDI gastrointestinal system subscore change from baseline by year.........................68

Table 53. Fisher’s test for belimumab versus SoC SDI gastrointestinal system subscore change

from baseline .....................................................................................................................68

Table 54. SDI musculoskeletal system subscore change from baseline by year .......................69

Table 55. Fisher’s test for belimumab versus SoC SDI musculoskeletal system subscore


Table 56. SDI skin system subscore change from baseline by year ..........................................70

Table 57. Fisher’s test for belimumab versus SoC SDI skin subscore change from baseline ....70

Table 58. SDI premature gonadal failure subscore change from baseline by year ....................71

Table 59. Fisher’s test for belimumab versus SoC SDI premature gonadal failure subscore


Table 60. SDI diabetes subscore change from baseline by year ...............................................71

Table 61. Fisher’s test for belimumab versus SoC SDI diabetes subscore change from baseline

..........................................................................................................................................72

Table 62. SDI malignancy subscore change from baseline by year...........................................72

Table 63. SDI cardiovascular system subscore change from baseline ......................................73

Table 64. SDI diabetes subscore change from baseline ............................................................74

Table 65. SDI gastrointestinal system subscore change from baseline .....................................74

Table 66. SDI premature gonadal failure subscore change from baseline .................................75

Table 67. SDI malignancy subscore change from baseline .......................................................76

Table 68. SDI musculoskeletal system subscore change from baseline ....................................77

Table 69. SDI neuropsychiatric system subscore change from baseline ...................................77

Table 70. SDI ocular system subscore change from baseline ...................................................78

Table 71. SDI peripheral vascular system subscore change from baseline ...............................79

Table 72. SDI pulmonary system subscore change from baseline ............................................79

Table 73. SDI renal system subscore change from baseline .....................................................80

Table 74. SDI skin system subscore change from baseline.......................................................81

Table 75. Regression model AMS through year 5 with decade of baseline ...............................82

Table 76. Regression model AMS through year 5 without decade of baseline ..........................82

10

Table 77. Regression model cumulative corticosteroid usage through year 5 controlled for

decade of entry..................................................................................................................83

Table 78. Regression model cumulative corticosteroid usage through year 5 without decade of

entry covariates .................................................................................................................83

Table 79. Results of full propensity score logistic regression model, pooled LTE and TLC

dataset with 5 years follow-up (N=973) ..............................................................................85

Table 80. Summary statistics of PSM variable, pooled LTE and TLC dataset with 5 years follow-

up (N=973) ........................................................................................................................85

Table 81. Bias prior to PS matching, pooled LTE and TLC dataset with 5 years follow-up

(N=973) .............................................................................................................................87

Table 82. Bias post PS matching, pooled LTE and TLC dataset with 5 years follow-up (n=362)

..........................................................................................................................................88


dataset with ≥ 1 year follow-up (N=1541) ..........................................................................89

Table 84. Summary statistics of PSM variable, pooled LTE and TLC dataset with ≥ 1 year

follow-up (N=1541) ............................................................................................................89

Table 85. Bias prior to PS matching, pooled LTE and TLC dataset with ≥ 1 year follow-up

(N=1541) ...........................................................................................................................91

Table 86. Bias post PS matching, pooled LTE and TLC dataset with ≥ 1 year follow-up (n=646)

..........................................................................................................................................92

Table 87. Year 5 Total SDI difference of change from baseline controlled for entry decade and

antimalarial use .................................................................................................................93


Table 89. Proportional hazards model of time to first change in total SDI score controlling for

baseline SDI score and decade of study entry, exponential distribution .............................95

Table 90. Proportional hazards model of time to first change in total SDI score, exponential

distribution .........................................................................................................................95


distribution. ........................................................................................................................95


distribution. ........................................................................................................................96

Table 93. Accelerated failure time model of time to first change in total SDI score, loglogistic

distribution .........................................................................................................................96

Table 94. Accelerated failure time model of time to first change in total SDI score, lognormal

distribution .........................................................................................................................96

11

Table 95. Fit of regression models of time to first change in total SDI score ..............................97

Table 96. SDI change from baseline .........................................................................................97



Table 99. SDI change from baseline constrained model year 3 ............................................... 100

Table 100. SDI change from baseline constrained model year 4 ............................................. 101

Table 101. SDI change from baseline constrained model year 5 ............................................. 102

Table 102. Year 1 Total SDI difference of change from baseline controlled for entry decade and

antimalarial use at baseline ............................................................................................. 103

Table 103. Year 1 Total SDI difference of change from baseline controlled for entry decade .. 104

Table 104. Year 1 Total SDI difference of change from baseline controlled for antimalarial use at

baseline ........................................................................................................................... 105

Table 105. Year 1 Total SDI difference of change from baseline ............................................. 105





Table 110. Annual transition probabilities ................................................................................ 108

Table 111. SDI ocular system subscore change from baseline by year .................................. 108

Table 112. Fisher’s test for belimumab versus SoC SDI ocular subscore change from baseline

........................................................................................................................................ 109

Table 113. SDI neuropsychiatric system subscore change from baseline by year ................... 109


change from baseline ...................................................................................................... 110

Table 115. SDI renal system subscore change from baseline by year .................................... 110

Table 116. Fisher’s test for belimumab versus SoC SDI renal system subscore change from

baseline ........................................................................................................................... 111

Table 117. SDI pulmonary system subscore change from baseline by year ............................ 111

Table 118. Fisher’s test for belimumab versus SoC SDI pulmonary system subscore change

from baseline ................................................................................................................... 112

Table 119. SDI cardiovascular system subscore change from baseline by year ...................... 112

Table 120. Fisher’s test for belimumab versus SoC SDI cardiovascular subscore change from

baseline ........................................................................................................................... 113

Table 121. SDI peripheral vascular system subscore change from baseline by year .............. 113

12

Table 122. Fisher’s test for belimumab versus SoC SDI peripheral vascular system subscore


Table 123. SDI gastrointestinal system subscore change from baseline by year..................... 114

Table 124. Fisher’s test for belimumab versus SoC SDI gastrointestinal system subscore


Table 125. SDI musculoskeletal system subscore change from baseline by year ................... 115



Table 127. SDI skin system subscore change from baseline by year ...................................... 116

Table 128. Fisher’s test for belimumab versus SoC SDI skin change from baseline ............... 116

Table 129. SDI premature gonadal failure subscore change from baseline by year ................ 117

Table 130. Fisher’s test for belimumab versus SoC SDI premature gonadal failure subscore


Table 131. SDI diabetes subscore change from baseline by year ........................................... 117

Table 132. Fisher’s test for belimumab versus SoC SDI diabetes subscore change from

baseline ........................................................................................................................... 118

Table 133. SDI malignancy subscore change from baseline by year ....................................... 118

Table 134. Fisher’s test for belimumab versus SoC SDI malignancy subscore change from

baseline ........................................................................................................................... 119

Table 135. SDI cardiovascular system subscore change from baseline .................................. 120

Table 136. SDI diabetes change from baseline ....................................................................... 121

Table 137. SDI gastrointestinal system subscore change from baseline ................................. 122

Table 138. SDI premature gonadal failure subscore change from baseline ............................. 122

Table 139. SDI malignancy subscore change from baseline ................................................... 123

Table 140. SDI musculoskeletal system subscore change from baseline ................................ 124

Table 141. SDI neuropsychiatric system subscore change from baseline ............................... 125

Table 142. SDI ocular system subscore change from baseline ............................................... 126

Table 143. SDI peripheral vascular system subscore change from baseline ........................... 126

Table 144. SDI pulmonary system subscore change from baseline ........................................ 127

Table 145. SDI renal system subscore change from baseline ................................................. 128

Table 146. SDI skin system subscore change from baseline ................................................... 129

Table 147. Paired t-test for matched subjects’ 5th year days from baseline ............................. 132

Table 148. Paired t-test for matched subjects’ 5th year days from baseline ............................ 134

Table 149. Comparison of US LTE and TLC baseline characteristics between patients with 5

years follow-up and discontinuers.................................................................................... 134

13

Table 150. Comparison of pooled LTE and TLC baseline characteristics between patients with 5

years follow-up and discontinuers.................................................................................... 135

Table 151. US LTE and TLC baseline SDI organ system subscore counts ............................. 136

Table 152. Pooled LTE and TLC baseline SDI organ system subscore counts ....................... 137

Table 153. Mean and median of US LTE and TLC baseline variables ..................................... 139

Table 154. Mean and median of pooled LTE and TLC baseline variables ............................... 140

Table 155. US LTE and TLC proportional hazards model of time to first change in total SDI

score, exponential distribution ......................................................................................... 140

Table 156. Pooled LTE and TLC proportional hazards model of time to first change in total SDI

score, exponential distribution ......................................................................................... 141

Table 157. Mild/moderate flares (lognormal accelerated failure time model) ........................... 141

Table 158. Severe flares (lognormal accelerated failure time model) ...................................... 142

Table 159. US LTE and TLC total SDI score change from baseline at 76 weeks .................... 142

Table 160. Pooled LTE and TLC total SDI score change from baseline at 52 or 76 weeks. .... 143

Table 161. US LTE proportional hazards model of time to first change in total SDI score,

exponential distribution .................................................................................................... 144

Table 162. Pooled LTE proportional hazards model of time to first change in total SDI score,

exponential distribution .................................................................................................... 144

Table 163. Mild/moderate flares (lognormal accelerated failure time model) ........................... 145

Table 164. Severe flares (lognormal accelerated failure time model) ...................................... 145

14

List of Abbreviations

Abbreviation Definition

ACR American College of Rheumatology

BEL112233 Long term extension study of belimumab IV plus SoC conducted in the US

AMS Adjust mean SLEDAI

BLISS Study of Belimumab in Subjects with SLE

BLISS-52 52-week RCT of belimumab IV plus SoC compared with SoC alone

BLISS-76 76-week RCT of belimumab IV plus SoC compared with SoC alone

CI Confidence interval

CV Cardiovascular

GSK GlaxoSmithKline

LTE Long term extension study

OLS Ordinary least squares

PS Propensity score

PSM Propensity score matching

RCT Randomized controlled trial

SD Standard deviation

SDI SLICC/ACR Damage Index

SE Standard error

SELENA Safety of Estrogens in Lupus Erythematosus National Assessment

SELENA-SLEDAI SELENA modification to the SLEDAI

SF-20 20-Item Short Form Survey

SF-36 36-Item Short Form Survey

SLE Systemic lupus erythematosus

SLEDAI Systemic Lupus Erythematosus Disease Activity Index

SLICC Systemic Lupus International Collaborating Clinics

SoC Standard of care

Std Diff Standardized difference

TLC Toronto Lupus Cohort

US United States

ΔSDI SDI change from baseline

15

Executive Summary

Two Phase 3 randomized controlled trials of intravenously (IV) administered belimumab have

established the clinical efficacy of belimumab plus standard of care (SoC) versus SoC alone at

52 (BLISS 52) and 76 (BLISS 76) weeks in the treatment of systemic lupus erythematosus

(SLE). A pooled analysis (201223) of the US long term extension (LTE) (BEL112233) and

outside US LTE (BEL112234) trials reported low levels of organ damage accrual in patients who

received belimumab plus SoC for 5 years (measured by the Systemic Lupus International

Collaborating Clinics [SLICC]/American College of Rheumatology Damage Index [SDI]).

However, because the LTEs did not have SoC comparator arms, the pooled analysis could not

provide a statistical comparison of belimumab plus SoC versus SoC alone. Thus, the question

of the long-term relative efficacy in SLE of belimumab with SoC versus SoC alone remained

unanswered.

The purpose of this study was to provide a long-term comparative analysis. It did so by

comparing BLISS LTE patients to propensity score-matched (PSM) SLE patients with similar

baseline characteristics taken from an external SLE cohort.

A systematic review identified the Toronto Lupus Cohort (TLC) as the preferred source of SoC

data for this study. Criteria included the size of the cohort, the extent of organ damage, and

severity of SLE disease activity comparable to the inclusion criteria of the BLISS clinical trials.

Longitudinal individual patient-level data was obtained from TLC for 706 patients with a

Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score ≥ 6, among other

criteria.

PSM was carried out in the US LTE/TLC dataset using 14 clinical predictors (17 operationalized

variables) of SLE organ damage and in the pooled LTE/TLC dataset using 12 clinical predictors

of SLE organ damage. (One predictor was missing from the outside US data.) Ninety-nine of

195 belimumab patients were matched 1:1 to 99 of 381 TLC patients for US LTE/TLC endpoints

requiring 5 years follow-up. 179 of 259 belimumab patients were matched 1:1 to 179 of 592 TLC

patients for US LTE/LTC time-to-event endpoints requiring ≥ 1 year follow-up. 181 of 592

belimumab patients were matched 1:1 to 181 of the 381 TLC patients for pooled LTE/TLC

endpoints requiring 5 years follow-up. 323 of 949 belimumab patients were matched 1:1 to 323

of the 592 TLC patients for pooled LTE/TLC time-to-event endpoints requiring ≥ 1 year follow-

up.

The primary endpoint was the change from baseline to 5th year visit of the SLICC/ACR Damage

Index (SDI) in the US LTE/TLC dataset. The first secondary endpoint was a time-to-event

16

analysis of first increase in SDI in the US LTE/TLC dataset. The first exploratory analyses used

the same endpoints in the pooled LTE/TLC dataset.

The primary analysis in the US LTE/TLC dataset found the change in total SDI score from

baseline to 5th-year visit was significantly lower (-0.4343, p<0.001) for patients taking belimumab

plus SoC versus SoC alone. The time-to-event analysis in the US LTE/TLC dataset found the

time to first change in total SDI score was significantly slower (HR = 0.3991, p<0.001,

exponential distribution) for patients taking belimumab plus SoC versus SoC alone.

The results in the pooled LTE/TLC dataset were similar. The change in total SDI score from

baseline to 5th-year visit was significantly lower (-0.4530, p<0.001) for patients taking belimumab

plus SoC versus SoC alone. The time-to-event analysis found the time to first change in total

SDI score was significantly slower (HR=0.3968, p<0.001, exponential distribution) for patients

taking belimumab plus SoC versus SoC alone.

Additional secondary and exploratory analyses were performed with SDI organ system-specific

subscores. Similar results were seen. The low numbers of events in these analyses, however,

suggest the organ system-specific results be regarded with caution.

17

1 Introduction

Two Phase 3 randomized controlled trials of intravenously (IV) administered belimumab have

established the clinical efficacy of belimumab plus standard of care (SoC) versus SoC alone at

52 (BLISS 52) and 76 (BLISS 76) weeks. The US long term extension (LTE)

(BEL112233/NCT00724867) and outside US LTE (BEL112234/NCT00712933) of these trials,

however, did not have comparison SoC arms. A pooled analysis of the LTEs (201223) reported

low levels of organ damage accrual in patients who received belimumab plus SoC for 5 years

(measured by the Systemic Lupus International Collaborating Clinics [SLICC]/American College

of Rheumatology Damage Index [SDI]).1 However, because the LTEs did not have SoC

comparator arms, study 201223 could not provide a statistical comparison of belimumab plus

SoC versus SoC alone. Thus, the question of the long-term relative efficacy of belimumab with

SoC versus SoC alone remained unanswered.

The purpose of this study was to provide a long-term comparative analysis between belimumab

plus SoC versus SoC alone in the treatment of systemic lupus erythematosus (SLE). It planned

to do so by comparing BLISS LTE patients to propensity score-matched (PSM) SLE patients

with similar baseline characteristics taken from an external SLE cohort.

A systematic review of the literature was previously performed to identify research cohorts of

SLE patients (attached as Appendix A).2 The review identified the Toronto Lupus Cohort (TLC)

as the preferred source of SoC data for this study based on the size of the cohort, the extent of

organ damage seen in the patients and severity of SLE disease activity. A subset of the TLC

with patient baseline characteristics similar to the BLISS trials had previously been used in a

GSK study of mortality and damage progression in SLE. A similar subset of the TLC was

envisioned in this study.

This was the first analysis of long term efficacy of belimumab plus SoC versus SoC alone. The

primary analysis took place on data from the US LTE trial (BEL112233) versus the TLC patient

data. (It was performed on data from the US LTE because the US dataset offered more clinical

matching variables and included 5th-year visits.) As an exploratory sensitivity analysis, the

same analysis was performed on the pooled data of the two BLISS LTE trials (BEL112233 and

BEL112234) versus the TLC.

Throughout the remainder of this document “belimumab treatment” refers to treatment with

belimumab supplemented by SoC while SoC refers to SoC alone. Similarly, “TLC” throughout

the remainder of this document refers to a subset of the TLC with patient characteristics similar

to the patient baseline characteristics in the BLISS trials.

18

2 Objectives

2.1 Primary Objective

To compare the mean change in SDI scores from baseline (index date) to year 5 between

patients treated with belimumab or SoC, based on data from the US BLISS LTE trial

(BEL112233) and the TLC.

2.2 Secondary Objectives

To compare the time to first SDI worsening between patients treated with belimumab or SoC,

based on data from the US BLISS LTE trial (BEL112233) and the TLC.

To compare the total SDI score at yearly intervals between patients treated with belimumab or

SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC.

To perform multi-state Markov modeling transition analyses of SDI independently for the

belimumab and SoC groups using the Jackson et al. 2011 methodology based on data from the

US BLISS LTE trial (BEL112233) and the TLC.3,4

To describe the change from baseline in SDI organ damage system (ocular, neuropsychiatric,

renal, pulmonary, cardiovascular, peripheral vascular, gastrointestinal [GI], musculoskeletal,

skin, premature gonadal failure, diabetes and malignancy) summarized by year interval of

patients treated with belimumab or SoC, based on data from the US BLISS LTE trial

(BEL112233) and the TLC.

2.3 Exploratory Objectives

As a sensitivity analysis, the primary and secondary objectives above were retested using

pooled data from the BLISS LTE trials (BEL112233 and BEL112234) and the TLC.

3 Selection of the External SLE Cohort

A full report of the selection of the external SLE cohort is attached as Appendix A.2 Briefly, a

systematic literature review was performed to identify cohorts, registries or other databases

formed to support studies in SLE. The objective was to identify an SLE comparison cohort with

population characteristics similar to the BLISS trial population and with an adequate sample of

patients with complete clinical data and at least five years follow-up. Three hundred ninety-three

publications were identified referring to 92 cohorts. Twenty-one cohorts/databases of

approximately 400 or more patients were identified which had been studied in at least 3

19

publications. Data for each of these 21 cohorts were extracted from 317 publications to fill a

data extraction form of 53 items. Evaluation criteria included cohort size, ethnicity, age, duration

of SLE, severity of disease activity, extent of organ damage progression, duration of follow-up,

loss to follow-up, scope of data collection and data availability.

The review identified the Toronto Lupus Cohort (TLC) as the preferred source of SoC data for

this study based on the size of the cohort, the extent of organ damage seen in the patients and

severity of SLE disease activity, which was comparable to the BLISS LTE trial inclusion criteria.

The TLC collects over 500 data points at each visit with additional data collected on an annual

basis. Moreover, the scales for disease severity, organ damage progression and health-related

quality of life were compatible with those used in the BLISS trials (Table 1). A subset of the TLC

with patient baseline characteristics similar to the BLISS trials had previously been used in a

GlaxoSmithKline (GSK) study of mortality and damage progression in SLE.1

Table 1. Comparable instruments used for the BEL112233 LTE and TLC

BEL112233 LTE Toronto Lupus Cohort

SLE Disease Activity SELENA-SLEDAI SLEDAI-2K

SLE Organ Damage SDI SDI

Health-related Quality of Life SF-36 Version 2 SF-36 Version 1

Abbreviations: SDI, SLICC/ACR Damage Index; SELENA, Safety of Estrogens in Lupus Erythematosus National Assessment; SF-36, 36-Item Short Form Survey; SLE, systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index

4 Population

Eligibility criteria from the BLISS trials were applied to the patients from the TLC (Table 2).

Application of these criteria produced the raw sample sizes seen in Table 3 at baseline and 1, 2

and 5-years of follow-up.

20

Table 2. Eligibility criteria.

Inclusion Criteria

Diagnosis of systemic lupus erythematosus (ICD-9 710.0) using ≥ 4 of 11 American College of Rheumatology criteria (710.0)

≥ 18 years of age

SELENA SLEDAI/SLEDAI-2K score ≥ 6 at baseline

Auto-antibody positive (anti-nuclear antibody ≥ 1:80 and/or anti-dsDNA ≥ 30 IU/mL)

Exclusion Criteria

Active severe lupus nephritis or central nervous system lupus

Receipt of B cell target therapy at any time

Abbreviations: SELENA, Safety of Estrogens in Lupus Erythematosus National Assessment; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index

Table 3. Raw sample sizes in BEL112233, BEL112234 and the TLC

Sample Sizes

With ≥ 1 Year

Follow up

With ≥ 2 Years

Follow up

With ≥ 5 Years

Follow up

For Time to Event

Analyses

Toronto Lupus Cohort 940 817 546 940

Year 1-2 Year 2-3 Year 5-6 NA

BEL112233 (US BLISS LTE) 259 252 195 259

Pooled dataset

(BEL 112233, BEL112234) 949 871 592 949

Abbreviations: LTE, long term extension

The baseline date in BEL112233 and BEL112234 was set as the date of first exposure to

belimumab. For patients in the TLC, the baseline date was the first date the patient’s SLEDAI-

2K score reached or exceeded 6.

TLC patients were also excluded for the following reasons:

• Baseline date before 1990 (due to changes in care patterns prior to 1990)

• ≥15 or years follow-up

• No visit within 24 weeks of annual visit timing (due to irregularity of TLC annual visits)

With these exclusions, the sample used for the analyses below included 259 patients from

BEL112233, 949 patients from the pooled BEL 112233 and BEL112234 datasets and 592

patients from the TLC.

21

Table 4. Sample sizes in BEL112233, BEL112234 and the TLC for 5 year analyses and

time to event analyses.

Sample Sizes

With ≥ 1 Year

Follow up

With ≥ 2 Years

Follow up

With ≥ 5 Years

Follow up

For Time to Event

Analyses

Toronto Lupus Cohort 592 499 381 592

Year 1-2 Year 2-3 Year 5-6 NA

BEL112233 (US BLISS LTE) 259 252 195 259

Pooled dataset

(BEL 112233, BEL112234) 949 871 592 949

Abbreviations: LTE, long term extension

5 Methods

5.1 Choice of Propensity Score Matching Variables

Predictors of SLE organ damage were chosen for propensity score matching (PSM) variables. A

recent systematic literature review identifying factors influencing organ damage and damage

progression5 was used to identify publications which reported predictors of SLE organ damage

progression.6–9 These were augmented by an internal GSK study which studied the impact of

disease activity on mortality and organ damage progression.10 The predictors found in the

literature (Table 5) were then reviewed by clinical experts and limited to those for which data

was available in both BEL112233 and the TLC. One variable was available – disease activity

over time – but was not suitable as a PSM variable because it was not a baseline variable. This

process produced the list of 14 PSM variables seen in the first column of Table 6. All 14

variables (17 operationalized variables) were used in the PSM for the primary and secondary

analyses (checked in the second column of Table 6). The exploratory analysis on the pooled

(BEL112233, BEL112234) dataset had 13 PSM variables available (checked in the third column

of Table 6). The PSM variable smoker was excluded from the exploratory analyses on the

pooled dataset due to an inexplicably large difference in proportions between the pooled and

TLC datasets; 2% versus 24%, respectively. The PSM variables were operationalized as 17

variables in the BEL112233 dataset (checked in the fifth column of Table 6) and as 16 variables

in the pooled dataset (checked in the sixth column of Table 6). Definitions of these

operationalized variables from both the US LTE and the TLC cohorts are provided in Table 7.

22

Baseline SDI was operationalized as a categorical variable because there were so few patients

with baseline SDI > 2. The references for the operationalized Race/Ethnicity and Baseline SDI

variables were Caucasian and zero, respectively.

Table 5. Predictors of organ damage found in the literature

Predictors

Age6–8,10

Gender7,8,10

Race/Ethnicity7,10

Household income7

Educational attainment7

SLE duration7,9,10

History - hypertension7

History - dyslipidemia10

History - proteinuria7

History - lupus anticoagulant positivity7

History - anticardiolipin positivity7

History - anti-β2-glycoprotein I positivity7

History – anti-Ro positivity7

Current smoker10

Number of ACR criteria satisfied at diagnosis7

Baseline SLEDAI score8

Disease activity over time (i.e., time-weighted SLEDAI)6,8,9

Corticosteroid use/dose6,7,10

Hydroxychloroquine/other antimalarial drug use7,10

Cyclophosphamide/other immunosuppressive use7,10

Initial or prior SDI6,9

SF-20 physical functioning9

Abbreviations: ACR, American College of Rheumatology; SDI, SLICC/ACR Damage Index; SF-20, 20-Item Short Form Survey; SLE, systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index

23

Table 6. Propensity score baseline matching variables in the data and how they were operationalized.

PSM Variables Available in the Data

Primary/Secondary Analysis

Exploratory Analysis

Operationalized

Variables

Primary/Secondary Analysis

Exploratory Analysis

Age X X Age X X

Age squared X X

Gender X X Female X X

Race/Ethnicity X X Black * X X

Asian/Other Race * X X

SLE duration X X SLE duration X X

History - hypertension X X History - hypertension X X

History - dyslipidemia X X History - dyslipidemia X X

History - proteinuria X X History - proteinuria X X

Current smoker X Current smoker X

Number of ACR criteria satisfied at diagnosis

X X Number of ACR criteria satisfied at diagnosis

X X

Baseline SLEDAI score X X Baseline SLEDAI score X X

Corticosteroid use X X Corticosteroid use X X

Antimalarial use X X Antimalarial use X X

Immunosuppressive use X X Immunosuppressive use X X

Baseline SDI X X Baseline SDI = 1 ** X X

Baseline SDI = 2+ ** X X

Abbreviations: ACR, American College of Rheumatology; SDI, SLICC/ACR Damage Index; SLE, systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index * Caucasian is the reference ** SDI = 0 is the reference

24

Table 7. Definitions of variables

Operationalized

Variables Variable Type Long Term Extension Toronto Lupus Cohort

Age Continuous Calculated as the difference in years between date of birth and baseline date

Calculated as the difference in years between date of birth and baseline date

Age squared Continuous Calculated as the Age squared Calculated as the Age squared

Female True/False True if Female True if Female

Black * True/False True if subject’s race was Black True if subject’s race was Black

Asian/Other Race * True/False True if subject’s race was neither White nor Black

True if subject’s race was neither White nor Black

SLE duration Continuous Calculated as the difference in years between date of diagnosis and baseline date

Calculated as the difference in years between date of diagnosis and baseline date

Hypertension True/False True if SBP at >140 or DBP at >90 or history of antihypertensive therapy or history of adverse events related to hypertension

True if SBP>140 or DBP>90 or on antihypertensive therapy at baseline

Dyslipidemia True/False True if history of hyperlipidemia therapy or history of adverse events related to high cholesterol

True if total cholesterol at > 5.2 mmol/L or on hyperlipidemia therapy at baseline

Proteinuria True/False True if baseline proteinuria at > 500 mg/day True if baseline proteinuria at > 500 mg/day

Current smoker True/False True if tobacco user at baseline True if tobacco user at baseline

Number of ACR criteria satisfied at diagnosis

Integer Total ACR criteria at baseline Total ACR criteria at baseline

Baseline SLEDAI score Continuous Total SELENA-SLEDAI at baseline Total SLEDAI-2K at baseline

Corticosteroid use True/False True if taking any corticosteroid at baseline True if taking any corticosteroid at baseline

Antimalarial use True/False True if taking any antimalarial medication at baseline

True if taking any antimalarial medication at baseline

Immunosuppressive use True/False True if taking any immunosuppressive medication at baseline

True if taking any immunosuppressive medication at baseline

Baseline SDI = 1 ** True/False True if total SDI score at baseline is equal to 1 True if total SDI score at baseline is equal to 1

25

Baseline SDI = 2+ ** True/False True if total SDI score at baseline is greater

than or equal to 2 True if total SDI score at baseline is greater than or equal to 2

Abbreviations: ACR, American College of Rheumatology; DBP, diastolic blood pressure; mmol/L, millimoles per liter; SBP, systolic blood pressure; SDI, SLICC/ACR Damage Index; SELENA, Safety of Estrogens in Lupus Erythematosus National Assessment; SLE, systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index * Caucasian is the reference ** SDI = 0 is the reference

26

5.2 Propensity Score Matching Methods

Propensity scores were calculated using the logistic regression procedure in SAS Version 9.411

in the following manner:

• The model specification initially included all potential predictor variables in Table 6 as

independent variables (age squared was also added, as well as dummy variables for

race/ethnicity and baseline SDI strata) (Full Model)

• In a backward elimination step-wise fashion, the statistically least significant predictor

was dropped from the propensity score model, until all included predictors had a p-value

< 0.1 (Trimmed Model)

• The specific predictors of organ damage included as covariates in the trimmed model

was based on the model specification with the minimum Akaike information criterion

(AIC) value.

• Attention was devoted to assessing the adequacy of the match for baseline SDI score

(as likely the most important predictor of future organ damage), by comparing the

frequency distribution of baseline SDI scores for the belimumab and SoC samples.

The propensity score (PS) value for matching was defined as the estimated log-odds (i.e., the X

value) from the logistic regression, rather than the predicted probability, to enhance the range of

variation the PS distribution for matching. LTE patients were matched 1:1 to TLC patients

based on similar PS value (within a caliper1 value defined as 20% of the standard deviation for

the distribution of the PS variable in the full sample). Unmatched patients were excluded from

the analysis of the PS-matched patient sample. The matching process was implemented using

a commonly used SAS macro.12

Four sets of matches were performed. For the primary and secondary analysis, matching was

performed on the BEL112233 data for 1) the analyses requiring 5 years of follow-up and 2)

time-to-event analyses requiring ≥ 1 year of follow-up. Likewise, for the exploratory analysis,

matching was performed on the pooled data for 1) analyses requiring 5 years of follow-up and

2) time-to-event analyses requiring ≥ 1 year of follow-up.

PSM was performed twice, once with a PS value based on a model with all candidate covariates

(full model), and then with a PS value based on a model with selected covariates deleted

(trimmed model), as described above. Post-PSM balance in covariates using the full-model PS

1 the maximum permitted difference between matched subjects

27

was superior to balance using the trimmed model PS, so the former sample was selected for all

PS-matched sample analyses.

5.3 Assessment of Post PSM Covariate Balance

The measure of balance (bias) used was the standardized distance across the variables used to

determine the PS value for each patient. Standardized distance is defined as

d = (X̅T - X̅

C) /√[Var(XT) + Var(XC)]/2

for continuous variables and

d = (P̂T - P̂

C) /√[(P̂T(1 − P̂T) + (P̂C(1 − P̂C)]/2

for binary variables.

Generally, for adequate balance the standardized distance should be no larger than 10% for all

variables used to determine PS values. Ideally the standardized distance would be less than

5% for all variables.13 Variables with standardized distances larger than 10% were added as

covariates to each analysis.

5.4 Visit Selection

Longitudinal data was available from both datasets for estimation of change in SDI. Visit

intervals in the BEL112233 and pooled datasets were fixed. In BEL112233 the final visit of the

parent trial was at 76 weeks. Thereafter SDI was recorded every 48 weeks while SLEDAI was

recorded every 24 weeks. Visits at weeks 52 and 76 of the parent trials and every 48 weeks

thereafter were selected as SDI “annual” visits, with a short 24-week “second year” between

weeks 52 and 76 (SDI was not recorded at the week 100 visit.) In the pooled dataset the final

visits of the parent trials were at 52 or 76 weeks. Depending on the parent trial, “annual” visits

occurred as in BEL112233 (weeks 52 and 76) or at weeks 52 and 100. Thereafter, annual visits

occurred every 48 weeks. In both cases, if the patient was assigned to placebo in the parent

trial “annual” visits were every 48 weeks after the start of the LTE.

In the TLC data, visits were not performed at precise time intervals. For the purposes of the time

to event analyses, “annual” visits were defined based on the interval from the baseline date to

the visit closest to each 48-week interval and deviating no more than 24 weeks from that

interval.

28

For other SDI analyses, TLC “annual” visits were the visits that most closely matched the

intervals from baseline date of the “annual” visits of the LTE patients to whom they were

matched.

All SDI analyses were based only on “annual” visits, omitting other visits in each dataset. The

secondary analyses of mean SLEDAI and corticosteroid usage over 5 years include all visits

through the 5th “annual” visit in all three datasets. Since SLEDAI and corticosteroid usage was

recorded every 24 weeks in BEL112233, these 5 year analyses were through 240 weeks for

patients receiving placebo and 244 weeks for patients receiving belimumab in the parent study.

5.5 Primary and Secondary Endpoints

All inferential statistics were two-tail tests performed with an alpha of p=0.05.

5.5.1 Primary endpoint: difference in change of SDI from baseline to 5 years

Change of total SDI from baseline to 5 years was evaluated using linear regression with change

of total SDI from baseline as the dependent variable, and with a variable indicating treatment

group (belimumab or SoC). Unbalanced matching variable(s) determined via Section 5.3 above

were added as covariates. If statistically significant, the decade of entry into the study was also

a covariate. As a sensitivity analysis, the primary endpoint was also evaluated using inverse

propensity score weighting (IPSW), a PS method that uses the entire sample and the PS to

weight the observations, to confirm the robustness of results.

5.5.2 Difference in time to first SDI worsening

The time to the first worsening (increase) in total SDI score were analyzed using parametric

survival models with a binary indicator for treatment with belimumab as the covariate.

Unbalanced matching variable(s) determined via Section 5.3 above were added as covariates. If

statistically significant, the decade of entry into the study was also added as a covariate.

5.5.3 Change from baseline SDI score by year interval

Descriptive statistics of the change from baseline SDI score were estimated at the end of years

1 through 5 for both the belimumab and SoC groups. The counts and proportions of subjects in

each treatment arm, out to year 5, of the incremental changes from baseline of total SDI were

calculated.

Also, a continuation-ratio logit model was used to analyze the change from baseline for total SDI

score for each year. A binary indicator for treatment with belimumab was the only covariate. All

29

increases in total SDI ≥2 were combined into one category. Continuation-ratio logits were used

to model (1) the probability of any change (ΔSDI > 0) from baseline for a given year, and (2) the

conditional probability of a change greater than one given that there was a change from

baseline. The unconstrained continuation-ratio model allows for unequal odds ratios for the two

transitions.

Let 𝜋0 = P(ΔSDI = 0), 𝜋1= P(ΔSDI = 1), 𝜋+2 = P(ΔSDI ≥ 2)

𝑃(ΔSDI = 0 | ΔSDI ≥ 0) =𝜋0

𝜋0+𝜋1+𝜋+2 𝑃(ΔSDI > 1 | ΔSDI ≥ 0) =

𝜋1+𝜋+2

𝜋0+𝜋1+𝜋+2

Logit1:

log[𝜋1+𝜋+2

𝜋0] = 𝛼1 + 𝛽1×𝑏𝑒𝑙𝑖𝑚𝑢𝑚𝑎𝑏

𝑃(ΔSDI = 1 | ΔSDI ≥ 1) =𝜋1

𝜋1+𝜋+2 𝑃(ΔSDI ≥ 2 | ΔSDI ≥ 1) =

𝜋+2

𝜋1+𝜋+2

Logit 2:

log[𝜋+2


The probabilities for each of the three categories can be directly retrieved from the two logit

formulas. For subjects treated with belimumab the probabilities of each category were derived

as follows:

𝑃(ΔSDI ≥ 1) = 𝜋1 + 𝜋2 =exp [𝛼1 + 𝛽1]

1 + exp [𝛼1 + 𝛽1]

𝜋0 = 1 - (𝜋1 + 𝜋2) = 1 −exp [𝛼1+𝛽1]

1+exp [𝛼1+𝛽1]

𝜋+2 = exp [𝛼2 + 𝛽2]×𝜋1 = (exp [𝛼2+𝛽2]

1+exp [𝛼2+𝛽2]) (

exp [𝛼1+𝛽1]

1+exp [𝛼1+𝛽1])

𝜋1 = (exp [𝛼1 + 𝛽1]

1 + exp [𝛼1 + 𝛽1]) (

1

1 + exp [𝛼2 + 𝛽2])

For a given year the multiplicative change in the odds of any increase from baseline in total SDI

score for subjects taking belimumab versus those receiving SoC is given by exp(𝛽1). For

subjects taking belimumab versus those receiving SoC, the multiplicative change in the odds of

an increase > 1 given that there was any increase is given by exp(𝛽2).

30

Using three categories to measure change in SDI leaves four degrees of freedom for each year

and thus the unconstrained model is also a saturated model. The fully constrained model

(𝛽1=𝛽2) specifies equality of odds ratios and is nested within the unconstrained model. The

deviance of the unconstrained model was used to test equality of odds ratios for the two

transitions. If the constrained model provided a poor fit the unconstrained model was fit and a

Wald test was used to test the significance of treatment effects.

5.5.4 Difference of change from baseline SDI by year interval

Change of SDI from baseline to end of years 1 through 5 were evaluated using linear regression

with change of SDI from baseline as the dependent variable, and with a variable indicating

treatment group (belimumab or SoC). Unbalanced matching variable(s) determined via Section

5.3 above were added as covariates. If statistically significant, the decade of entry into the study

was also added as a covariate.

5.5.5 Transition analysis of SDI from baseline over a 5-year interval

The SAP specified that multi-state Markov modelling transition analysis of SDI would be

performed independently for the belimumab and SoC groups using the Jackson et al. 2011

methodology.3,4 This methodology calculates transition probabilities between health states over

time, in this case health states defined by SDI strata.

Due to the number of empty cells in such a transition matrix, annual transition probabilities

instead were estimated based on the time to first SDI worsening analysis Section 5.5.2 above.

5.5.6 Change from baseline of SDI organ damage system subscores

Descriptive statistics of the change from baseline SDI organ system subscores were estimated

at the end of years 1 through 5 for the belimumab and SoC groups. The counts and proportions

of the incremental changes for each of the SDI organ system subscores were calculated.

Counts for any change from baseline were combined into one category and a two-sided Fisher’s

exact test was used to test for independence of no change from baseline in each SDI organ

system subscore based on treatment arm.

31

5.5.7 Frequency of increase from baseline of SDI organ damage system

subscores

The SAP specified that the frequency of increase of SDI organ system subscores from baseline

to censoring between patients treated with belimumab or SoC would be evaluated using logistic

regression with a variable indicating treatment group (belimumab or SoC) as the dependent

variable, and with the change of SDI organ system subscore from baseline and unbalanced

matching variable(s) determined via Section 5.3 above as covariates. The decade of entry into

the study would also be a covariate.

Because of the low frequencies of SDI organ damage system subscores and the non-

dichotomous nature of the values, changes from baseline for SDI organ system subscores were

instead analyzed using linear regression with the difference between the subject’s score in their

final year and their baseline score used as the response variable and an indicator variable for

treatment with belimumab as an independent variable. A categorical variable for the decade of

entry was included if statistically significant. Unbalanced matching variable(s) determined via

Section 5.3 above were added as covariates. The year from baseline was also included to

control for the length of time from baseline.

The data set for this analysis consisted of the matched patients from the propensity score

matching where subjects were not restricted to at least 5 years of follow-up. A second analysis

was performed using the smaller dataset of matched patients with 5 years follow-up. The results

from the second analysis were used to check the robustness of the results where subjects’

scores were recorded in different years.

5.5.8 Difference in mean SLEDAI score from baseline over a 5-year interval

Mean SLEDAI score from baseline through 5th year were evaluated using linear regression with

mean SLEDAI score as the dependent variable and with a variable indicating treatment group

(belimumab or SoC) as a covariate. Unbalanced matching variable(s) determined via Section

5.3 above were added as covariates. If statistically significant, the decade of entry into the study

was also added as a covariate.

32

5.5.9 Difference in cumulative corticosteroid usage from baseline over a 5-year

interval

Cumulative use of corticosteroids from baseline through year 5 was evaluated using linear

regression with cumulative corticosteroid use as the dependent variable and with a variable

indicating treatment group (belimumab or SoC) as a covariate. Unbalanced matching variable(s)

determined via Section 5.3 above were added as covariates. If statistically significant, the

decade of entry into the study was also added as a covariate.

5.6 Exploratory Endpoints

All inferential statistics were two-tail tests performed with an alpha of p=0.05 performed on the

pooled (BEL112233, BEL112234) LTE dataset.

5.6.1 Difference in change of SDI from baseline to 5 years

Change of total SDI from baseline to 5 years was evaluated using linear regression with change

of total SDI from baseline as the dependent variable, and with a variable indicating treatment

group (belimumab or SoC). Unbalanced matching variable(s) determined via 5.3 above were

added as covariates. If statistically significant, the decade of entry into the study was also a

covariate.


The time to the first worsening (increase) in total SDI score were analyzed using parametric

survival models with a binary indicator for treatment with belimumab as the covariate.

Unbalanced matching variable(s) determined via Section 5.3 above were added as covariates. If

statistically significant, the decade of entry into the study was also added as a covariate.


Descriptive statistics of the change from baseline SDI score were estimated at the end of years

1 through 5 for both the belimumab and SoC groups. The counts and proportions of subjects in

each treatment arm, out to year 5, of the incremental changes from baseline of total SDI were

calculated.

Also, a continuation-ratio logit model was used to analyze the change from baseline for total SDI

score for each year. A binary indicator for treatment with belimumab was the only covariate. All

increases ≥2 were combined into one category. Continuation-ratio logits were used to model (1)

33

the probability of any change (ΔSDI > 0) from baseline for a given year, and (2) the conditional

probability of a change greater than one given that there was a change from baseline. The

unconstrained continuation-ratio model allows for unequal odds ratios for the two transitions.

Let 𝜋0 = P(ΔSDI = 0), 𝜋1= P(ΔSDI = 1), 𝜋+2 = P(ΔSDI ≥ 2)

𝑃(ΔSDI = 0 | ΔSDI ≥ 0) =𝜋0

𝜋0+𝜋1+𝜋+2 𝑃(ΔSDI > 1 | ΔSDI ≥ 0) =

𝜋1+𝜋+2

𝜋0+𝜋1+𝜋+2

Logit1:

log[𝜋1+𝜋+2


𝑃(ΔSDI = 1 | ΔSDI ≥ 1) =𝜋1

𝜋1+𝜋+2 𝑃(ΔSDI ≥ 2 | ΔSDI ≥ 1) =

𝜋+2

𝜋1+𝜋+2

Logit 2:

log[𝜋+2


The probabilities for each of the three categories can be directly retrieved from the two logit

formulas. For subjects treated with belimumab the probabilities of each category were derived

as follows:

𝑃(ΔSDI ≥ 1) = 𝜋1 + 𝜋2 =exp [𝛼1 + 𝛽1]

1 + exp [𝛼1 + 𝛽1]

𝜋0 = 1 - (𝜋1 + 𝜋2) = 1 −exp [𝛼1+𝛽1]

1+exp [𝛼1+𝛽1]

𝜋+2 = exp [𝛼2 + 𝛽2]×𝜋1 = (exp [𝛼2+𝛽2]

1+exp [𝛼2+𝛽2]) (

exp [𝛼1+𝛽1]

1+exp [𝛼1+𝛽1])

𝜋1 = (exp [𝛼1 + 𝛽1]

1 + exp [𝛼1 + 𝛽1]) (

1

1 + exp [𝛼2 + 𝛽2])

For a given year the odds of any increase from baseline in total SDI score for subjects taking

belimumab versus those receiving SoC is given by exp(𝛽1). For subjects taking belimumab

versus those receiving SoC the odds of an increase greater than 1 given that there was any

increase is given by exp(𝛽2).

Using three categories to measure change in SDI leaves four degrees of freedom for each year

and thus the unconstrained model is also a saturated model. The fully constrained model

34

(𝛽1=𝛽2) specifies equality of odds ratios and is nested within the unconstrained model. The

deviance of the unconstrained model was used to test equality of odds ratios for the two

transitions. If the constrained model provided a poor fit the unconstrained model was fit and a

Wald test was used to test the significance of treatment effects.


Change of SDI from baseline to end of years 1 through 5 were evaluated using linear regression

with change of SDI from baseline as the dependent variable, and with a variable indicating

treatment group (belimumab or SoC). Unbalanced matching variable(s) determined via Section

5.3 above were added as covariates. The decade of entry into the study was also a covariate.


The SAP specified that multi-state Markov modelling transition analysis of SDI would be

performed independently for the belimumab and SoC groups using the Jackson et al. 2011

methodology.3,4 This methodology calculates transition probabilities between health states over

time, in this case health states defined by SDI strata.

Due to the number of empty cells in such a transition matrix, annual transition probabilities

instead were estimated based on the time to first SDI worsening analysis Section 5.6.2 above.


Descriptive statistics of the change from baseline SDI organ system subscores were estimated

at the end of years 1 through 5 for the belimumab and SoC groups. The counts and proportions

of the incremental changes for each of the SDI organ system subscores were calculated.

Counts for any change from baseline were combined into one category and a two-sided Fisher’s

exact test was used to test for independence of no change from baseline in each SDI organ

system subscore based on treatment arm.


subscores

The SAP specified that the frequency of increase of SDI organ system subscores from baseline

to censoring between patients treated with belimumab or SoC would be evaluated using logistic

regression with a variable indicating treatment group (belimumab or SoC) as the dependent

variable, and with the change of SDI organ system subscore from baseline and unbalanced

matching variable(s) determined via Section 5.3 above as covariates. The decade of entry into

the study would also be a covariate.

35

Because of the low frequencies of SDI organ damage system subscores and the non-

dichotomous nature of the values, changes from baseline for SDI organ system subscores were

instead analyzed using linear regression with the difference between the subject’s score in their

final year and their baseline score used as the response variable and an indicator variable for

treatment with belimumab as an independent variable. A categorical variable for the decade of

entry was included if statistically significant. Unbalanced matching variable(s) determined via

Section 5.3 above were added as covariates. The year from baseline was also included to

control for the length of time from baseline.

The data set for this analysis consisted of the matched patients from the propensity score

matching where subjects were not restricted to at least 5 years of follow-up. A second analysis

was performed using the smaller dataset of matched patients with 5 years follow-up. The results

from the second analysis were used to check the robustness of the results where subjects’

scores were recorded in different years.


The SAP specified that mean SLEDAI score from baseline through year 5 would be evaluated

using linear regression with mean SLEDAI score as the dependent variable and with a variable

indicating treatment group (belimumab or SoC). Unbalanced matching variable(s) determined

via 5.3 above would be added as covariates. The decade of entry into the study would also be

a covariate.

The BEL 112234 dataset did not contain longitudinal SLEDAI scores. Therefore, this analysis

could not be undertaken.


interval

The SAP specified that cumulative use of corticosteroids from baseline through year 5 would be

evaluated using linear regression with cumulative corticosteroid use as the dependent variable

and with a variable indicating treatment group (belimumab or SoC). Unbalanced matching

variable(s) determined via 5.3 above would be added as covariates.

The BEL 112234 dataset did not contain enough longitudinal concomitant medication data for

this analysis to be feasible. Therefore, this analysis could not be undertaken.

5.7 Diagnostic Analyses

All inferential statistics were two-tail tests performed with an alpha of p=0.05.

36

5.7.1 Baseline characteristics of unmatched study arms

5.7.1.1 BEL112233 LTE and TLC

Comparisons of baseline characteristics were made using all subjects included in the

population. (See Section 4 above.) A separate analysis was performed for all subjects with at

least 5 years of follow-up as well as all subjects with at least 1 year of follow-up.

5.7.1.2 Pooled LTE and TLC

Comparisons of baseline characteristics was made using all subjects included in the population.

(See Section 4 above.) A separate analysis was also performed for all subjects with at least 5

years of follow-up as well as all subjects with at least 1 year of follow-up.

5.7.1.3 Comparison methods

Welch’s t-test was utilized to test for equality of means between study arm baseline

characteristics. The degrees of freedom for the test was approximated using the Welch-

Satterthwaite equation. The test statistic was calculated by the following formula:

t = �̅�𝑡−�̅�𝑐

√𝑠𝑡

2

𝑁𝑡+

𝑠𝑐2

𝑁𝑐

where:

• t (treatment) = belimumab

• c (control) = SoC

• �̅�𝑖 is the sample mean

• 𝑠𝑖2 is the sample variance

• 𝑁𝑖 is the number of subjects

The standardized difference is reported as the percent bias (%bias):

%bias =�̅�𝑡−�̅�𝑐

√(𝑠𝑡2+𝑠𝑐

2)/2

×100

5.7.2 Baseline characteristics of matched samples


Comparisons of baseline characteristics were made using all matched subjects. A separate

analysis was performed for matched subjects with at least 5 years of follow-up as well as all

subjects with at least 1 year of follow-up.

37


Comparisons of baseline characteristics were made using all matched subjects. A separate

analysis was performed for matched subjects with at least 5 years of follow-up as well as all

subjects with at least 1 year of follow-up.


Study arms were tested for statistically significant differences in patient baseline characteristics

using Welch’s t-test, Section 5.7.1.3 above. The standardized mean difference was also

determined for each covariate.

5.7.3 Distribution of year 5 data point timing

5.7.3.1 US BLISS LTE and TLC

Patients in the TLC were not seen at specific intervals. Likewise, patients originating in

belimumab and SoC arms of the underlying belimumab trials had different intervals of

observation. Therefore the 5th year observation in both arms took place at time points not

strictly 5 years from baseline. The distributions of time from baseline to the 5th year observation

were reported.

5.7.3.2 Pooled BLISS LTE and TLC

Patients in the TLC were not seen at specific intervals. Likewise, patients originating in

belimumab and SoC arms of the underlying belimumab trials had different intervals of

observation. Therefore, the 5th year observation in both arms took place at time points not

strictly 5 years from baseline. The distributions of time from baseline to the 5th year observation

were reported.


A paired t-test was used to test for differences between matched subjects in the length of

elapsed time from baseline.

5.7.4 Patients withdrawing from LTE and TLC cohorts


An analysis was conducted that included all study participants in both cohorts, i.e. subjects that

completed the full five years of follow-up as well as subjects that dropped out before study end.

The impact of the dropout rates were assessed by comparing those who completed the study

versus those who did not complete the study in terms of baseline and clinical characteristics.

38


An analysis was conducted that included all study participants in both cohorts, i.e. subjects that

completed the full five years of follow-up as well as subjects that dropped out before study end.

The impact of the dropout rates were assessed by comparing those who completed the study

versus those who did not complete the study in terms of baseline and clinical characteristics.


Time to event analyses were used to test for differences in clinical outcomes. The time to event

analysis consisted of: (1) time to first SDI change; (2) time to mild/moderate flare; and, (3) time

to severe flare. The BEL112234 dataset did not contain longitudinal flare data. Therefore, the

time to mild/moderate and severe flare analysis could not be undertaken with the pooled

sample.

5.7.5 BLISS LTE subjects randomized to SoC in parent trial


Analysis was conducted to test for study effects associated with differences in quality of care

obtained within the setting of the randomized clinical trials. These analyses were performed by

comparing BLISS subjects randomized to SoC against TLC patients to determine whether

enrollment in the randomized clinical trial had a significant effect on clinical outcomes

associated with SoC. Propensity score matching were used to match BLISS SoC subjects to

TLC patients based on the BLISS subjects’ characteristics at core baseline, i.e., at

randomization to SoC + placebo in BLISS 76 (BEL110751).


Analysis was conducted to test for study effects associated with differences in quality of care

obtained within the setting of the randomized clinical trials. These analyses were performed by

comparing BLISS subjects randomized to SoC against TLC patients to determine whether

enrollment in the randomized clinical trial had a significant effect on clinical outcomes

associated with SoC. Propensity score matching were used to match BLISS SoC subjects to

TLC patients based on the BLISS subjects’ characteristics at core baseline, i.e., at

randomization to SoC + placebo.

5.7.5.3 Comparison Methods

Fisher’s exact test was used to test for differences in SDI worsening during the parent study

compared with clinical outcomes in the TLC over a follow-up period equal to the duration of the

parent study.

39

5.7.6 Belimumab baseline of BLISS LTE subjects


Analysis was conducted to test for potential biases introduced by whether the patient was

randomized to belimumab or placebo in the parent study. This analysis focused on tests of

equivalence among the subgroups of BLISS LTE subjects randomized to belimumab 10 mg/kg

in the parent study, BLISS LTE subjects randomized to belimumab 1 mg/kg in the parent study,

and BLISS LTE subjects randomized to placebo in the parent study.


Analysis was conducted to test for potential biases introduced by whether the patient was

randomized to belimumab or placebo in the parent study. This analysis focused on tests of

equivalence among the subgroups of BLISS LTE subjects randomized to belimumab 10 mg/kg

in the parent study, BLISS LTE subjects randomized to belimumab 1 mg/kg in the parent study,

and BLISS LTE subjects randomized to placebo in the parent study.


Time to event analyses were used to test for differences in clinical outcomes during the follow-

up period of the comparative effectiveness analysis. The time to event analysis consisted of: (1)

time to first SDI change; (2) time to mild/moderate flare; and, (3) time to severe flare. The

BEL112234 dataset did not contain longitudinal flare data. Therefore, the time to mild/moderate

and severe flare analysis could not be undertaken with the pooled sample.

6 Results

6.1 Primary and Secondary Analyses

6.1.1 Propensity score matching

6.1.1.1 BEL112233 and TLC Patients with 5-years follow up

Table 8 and Table 9 show the results of the full propensity score logistic regression model over

the entire sample of 567 patients. The range of the PS distribution (Table 9) was -9.927 to

4.701. The range of common support (the range of “overlap” in the PS distributions) for the LTE

and TLC patient was -3.648 to 2.893, illustrated in Figure 1. With the caliper value of 0.53 (20%

of the standard deviation for the PS distribution), the range of support was -4.178 to 3.423. 95

TLC patients and the 11 LTE patients with PS values outside of the range of support (including

the caliper) could not be matched.

40

Using the PS values calculated from the full propensity score logistic regression model, 99 of

195 belimumab patients were matched 1:1 to 99 of the 372 TLC patients.


dataset with 5 years follow-up (N=567)

Parameter Odds Ratio SE z p-value

Intercept 0.000 0.000 -5.45 <0.001

Age 1.332 0.085 4.51 <0.001

Age Squared 0.997 0.001 -4.11 <0.001

Female 0.968 0.437 -0.07 0.943

Black 0.907 0.296 -0.3 0.765

Asian/Other Race 0.302 0.116 -3.13 0.002

SLE Duration 0.986 0.018 -0.76 0.449

Smoker 0.049 0.026 -5.73 <0.001

Hypertension 4.382 1.248 5.19 <0.001

Dyslipidemia 0.142 0.043 -6.42 <0.001

Proteinuria 0.234 0.086 -3.96 <0.001

ACR Criteria 1.181 0.114 1.72 0.085

Baseline SLEDAI 0.946 0.030 -1.77 0.076

Corticosteroid Use 1.505 0.430 1.43 0.153

Antimalarial Use 2.931 0.800 3.94 <0.001

Immunosuppressive Use 2.771 0.762 3.71 <0.001

Baseline SDI = 1 2.928 0.969 3.25 0.001

Baseline SDI = 2+ 4.920 1.850 4.24 <0.001

Abbreviations: ACR, American College of Rheumatology; SDI, SLICC/ACR Damage Index; SE, standard error; SLE, Systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; TLC, Toronto Lupus Cohort

Table 9. Summary statistics of PSM variable, BEL112233 and TLC dataset with 5 years

follow-up (N=567)

Statistic Value

Observations 567

Mean (SD) -1.365 (2.631)

Range -9.927, 4.701

Caliper (20% of SD) 0.53

Abbreviation: SD, standard deviation; TLC, Toronto Lupus Cohort

41

Figure 1. Common support in full model with all patients (n=567)

Prior to PSM, the LTE and TLC samples were not well balanced (Table 10). The percent bias is

larger than 10% for most of the variables (mean bias = 40%).

However, the PS-matched samples of 99 LTE and 99 TLC patients were well balanced (Table

11). Bias is less than 5% for nine of the seventeen variables, and less than 10% for all

variables (the mean bias is 4.6%).

-10

-50

5

Pre

dic

ted

PS

LTE TLC

42

Table 10. Bias prior to propensity score matching, BEL112233 and TLC dataset with 5

years follow-up (N=567)

Mean t-test

Variable Belimumab SoC % Bias t p>|t|

Age 42.769 37.303 45.5 5.01 <0.001

Age Squared 1947.4 1560.8 38.1 4.22 <0.001

Female 0.928 0.895 11.6 1.28 0.200

Black 0.231 0.153 19.7 2.29 0.022

Asian/Other Race 0.092 0.234 -39.0 -4.18 <0.001

SLE Duration 7.947 5.762 30.0 3.38 0.001

Smoker 0.036 0.237 -61.1 -6.27 <0.001

Hypertension 0.677 0.376 63.0 7.09 <0.001

Dyslipidemia 0.226 0.581 -77.5 -8.55 <0.001

Proteinuria 0.123 0.317 -48.1 -5.18 <0.001

ACR Criteria 5.923 5.651 19.8 2.22 0.027

Baseline SLEDAI 7.785 10.056 -48.4 -5.28 <0.001

Corticosteroid use 0.636 0.608 5.8 0.66 0.510

Antimalarial Use 0.738 0.519 46.6 5.17 <0.001

Immunosuppressive Use 0.538 0.315 46.4 5.31 <0.001

Baseline SDI = 1 0.272 0.148 30.7 3.60 <0.001

Baseline SDI = 2+ 0.287 0.108 46.2 5.55 <0.001

Abbreviations: ACR, American College of Rheumatology; PS, propensity score; SDI, SLICC/ACR Damage Index; SE, standard error; SLE, Systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; TLC, Toronto Lupus Cohort

43

Table 11. Bias post PS matching, BEL112233 and TLC dataset with 5 years follow-up

(n=198)

Mean t-test


Age 39.980 38.993 8.4 0.59 0.557

Age Squared 1733.0 1661.7 7.2 0.51 0.611

Female 0.929 0.919 3.8 0.27 0.790

Black 0.212 0.232 -4.8 -0.34 0.734

Asian/Other Race 0.141 0.121 6.0 0.42 0.676

SLE Duration 7.368 7.569 -2.6 -0.19 0.853

Smoker 0.071 0.071 0.0 0.00 1.000

Hypertension 0.545 0.535 2.0 0.14 0.887

Dyslipidemia 0.283 0.313 -6.6 -0.46 0.643

Proteinuria 0.202 0.182 5.1 0.36 0.720

ACR Criteria 6.030 5.939 6.5 0.46 0.648

Baseline SLEDAI 8.455 8.546 -2.2 -0.16 0.875

Corticosteroid use 0.646 0.667 -4.2 -0.30 0.766

Antimalarial Use 0.697 0.687 2.2 0.15 0.878

Immunosuppressive Use 0.455 0.444 2.0 0.14 0.887

Baseline SDI = 1 0.242 0.273 -6.9 -0.49 0.628

Baseline SDI = 2+ 0.152 0.182 -8.1 -0.57 0.570


6.1.1.2 BEL112233 and TLC patients with ≥ 1-year follow up for time to event analyses

Table 12 and Table 13 show the results of the full propensity score logistic regression model

over the entire sample of 965 patients. The range of the PS distribution (Table 13) was -8.475 to


and TLC patient was -3.928 to 2.171, illustrated in Figure 2. With the caliper value of 0.400

(20% of the standard deviation for the PS distribution), the range of support was -4.328 to

2.571. Two hundred and forty-six TLC patients and the 13 LTE patients with PS values outside

of the range of support (including the caliper) cannot be matched.



44


dataset with ≥ 1 year follow-up (N=965)


Intercept 0.000 0.000 -8.300 <.0001

Age 1.336 0.057 6.820 <.0001

Age Squared 0.997 0.000 -6.090 <.0001

Female 1.280 0.427 0.740 0.4580

Black 0.765 0.194 -1.050 0.2920

Asian/Other Race 0.260 0.075 -4.640 <.0001

SLE Duration 0.962 0.013 -2.840 0.0050

Smoker 0.069 0.028 -6.490 <.0001

Hypertension 1.709 0.361 2.540 0.0110

Dyslipidemia 0.421 0.093 -3.920 <.0001

Proteinuria 0.321 0.090 -4.070 <.0001

ACR Criteria 1.248 0.091 3.040 0.0020

Baseline SLEDAI 0.917 0.022 -3.540 <.0001

Corticosteroid use 1.375 0.277 1.580 0.1140

Antimalarial Use 2.118 0.420 3.780 <.0001

Immunosuppressive Use 2.530 0.510 4.610 <.0001

Baseline SDI = 1 3.186 0.788 4.690 <.0001

Baseline SDI = 2+ 4.618 1.264 5.590 <.0001


Table 13. Summary statistics of PSM variable, BEL112233 and TLC dataset with ≥ 1 year

follow-up (N=965)

Statistic Value

Observations 965

Mean (SD) -1.678 (2.027)

Range -8.475, 3.645


Abbreviation: SD, standard deviation; TLC, Toronto Lupus Cohort

45

Figure 2. Common support in full model with all patients (N=965)

Prior to PSM, the LTE and TLC samples are not well balanced (Table 14). The percent bias is

larger than 10% for most of the variables (mean bias = 35%).

However, the PS-matched samples of 179 LTE and 179 TLC patients are well balanced (Table

15). Bias is less than 5% for all but one variable, and less than 10% for all variables (the mean

bias is 2.2%).

46

Table 14. Bias prior to PS matching, BEL112233 and TLC dataset with ≥ 1 year follow-up

(N=965)

Mean t-test


Age 42.575 36.886 46.0 6.08 <0.001

Age Squared 1937.4 1541.0 37.6 5.01 <0.001

Female 0.934 0.888 16.3 2.13 0.033

Black 0.216 0.146 18.3 2.62 0.009

Asian/Other Race 0.093 0.280 -49.6 -6.26 <0.001

SLE Duration 7.746 6.208 21.5 2.91 0.004

Smoker 0.039 0.242 -61.2 -7.37 <0.001

Hypertension 0.533 0.380 31.1 4.31 <0.001

Dyslipidemia 0.228 0.347 -26.5 -3.55 <0.001

Proteinuria 0.135 0.330 -47.4 -6.10 <0.001

ACR Criteria 5.985 5.677 22.0 3.04 0.002

Baseline SLEDAI 7.857 10.030 -49.0 -6.37 <0.001

Corticosteroid use 0.649 0.625 5.0 0.68 0.494



Baseline SDI = 1 0.278 0.142 33.9 4.96 <0.001

Baseline SDI = 2+ 0.278 0.102 46.0 6.96 <0.001


47

Table 15. Bias post PS matching, BEL112233 and TLC dataset with ≥ 1 year follow-up

(N=358)

Mean t-test


Age 40.425 40.697 -2.4 -0.22 0.823

Age Squared 1763.4 1792.3 -3.0 -0.28 0.779

Female 0.916 0.916 0.0 0.00 1.000

Black 0.223 0.235 -2.7 -0.25 0.802

Asian/Other Race 0.128 0.128 0.0 0.00 1.000

SLE Duration 7.511 7.742 -3.2 -0.30 0.766

Smoker 0.056 0.067 -4.6 -0.44 0.661

Hypertension 0.458 0.458 0.0 0.00 1.000

Dyslipidemia 0.251 0.229 5.2 0.49 0.622

Proteinuria 0.168 0.179 -2.9 -0.28 0.781

ACR Criteria 5.955 5.927 1.9 0.18 0.855

Baseline SLEDAI 8.369 8.503 -3.7 -0.35 0.729


Antimalarial Use 0.659 0.670 -2.4 -0.22 0.823

Immunosuppressive Use 0.458 0.464 -1.1 -0.11 0.916

Baseline SDI = 1 0.246 0.257 -2.6 -0.24 0.808

Baseline SDI = 2+ 0.168 0.168 0.0 0.00 1.000


6.1.2 Primary endpoint – Difference in change in SDI from baseline to 5 years

The total SDI score change from baseline to 5 years was evaluated using linear regression with

a binary indicator for treatment with belimumab as a covariate. Ninety-nine BEL112233 patients

were matched to 99 TLC patients using PSM. All PSM variables were balanced (Table 11) so

none were added as covariates. The baseline decade of entry also was not significant, so it was

not added as a covariate.

The difference in total SDI score change from baseline to the 5th year for PSM patients was

significantly lower (-0.4343, p<0.001) for subjects taking belimumab.

48

Table 16. Year 5 Total SDI difference of change from baseline

Variable

OLS Coefficient (SE)

[95% CI] P value

Robust SE Coefficient (SE)

[95% CI] P value

Intercept 0.7172 (0.0886)

95% CI: [0.5425 ; 0.8918] p<0.001

0.7172 (0.1106) 95% CI: [0.5004 ; 0.9339]

p<0.001

Belimumab -0.4343 (0.1252)

95% CI: [-0.6813 ; -0.1874] p<0.001

-0.4343 (0.1188) 95% CI: [-0.6673 ; -0.2014]

p<0.001

Abbreviations: CI, confidence interval; OLS, ordinary least squares; SDI, SLICC/ACR Damage Index; SE, standard error

As a sensitivity analysis, results were also produced using the entire sample of 567 patients and

IPSW. The difference in total SDI from baseline to the 5th year was very similar to the PSM

results (Table 17). However, weighted bias was statistically inadequate, with bias greater than

10% for 9 of the 17 propensity score variables (Table 18). An additional regression augmented

IPSW analysis was produced, adding variables with bias > 10% (Table 19) to the regression

model. All three propensity score methods estimated a significantly lower (p < 0.001) increase in

SDI from baseline to the 5th year of -0.4343 to -0.4499 (Table 20), when comparing belimumab

treatment with SoC.

Table 17. Year 5 Total SDI difference of change from baseline using inverse propensity

score weighting (N=567)

Variable

Coefficient (SE)

[95% CI] P value

Belimumab -0.4405 (0.1163)

95% CI: [-0.6685 ; -0.2216] p<0.001

Abbreviations: CI, confidence interval; SDI, SLICC/ACR Damage Index; SE, standard error

49

Table 18. Bias using inverse propensity score weighting (N=567)

Standardized differences (% Bias)

Variable Raw Weighted

Age 45.5% 15.2%

Age Squared 38.1% 12.2%

Female 11.6% 11.4%

Black 19.7% 18.1%

Asian/Other Race -39.0% -4.6%

SLE Duration 30.0% 7.9%

Smoker -61.1% -3.3%

Hypertension 63.0% 28.8%

Dyslipidemia -77.5% -13.5%

Proteinuria -48.1% -12.0%

ACR Criteria 19.8% 7.4%

Baseline SLEDAI -48.4% 0.6%

Corticosteroid use 5.8% 8.3%

Antimalarial Use 46.6% 25.6%

Immunosuppressive Use 46.4% 11.6%

Baseline SDI = 1 30.7% 8.8%

Baseline SDI = 2+ 46.2% 7.3%

Abbreviations: ACR, American College of Rheumatology; SDI, SLICC/ACR Damage Index; SLE, Systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index

Table 19. Year 5 Total SDI difference of change from baseline using regression

augmented IPSW (N=567)

Variable

Coefficient (SE)

[95% CI] P value

Belimumab -0.4499 (0.1155)

95% CI: [-0.6763 ; -0.2234] p<0.001

Abbreviations: CI, confidence interval; IPSW, inverse propensity score weighting; SDI, SLICC/ACR Damage Index; SE, standard error

50

Table 20. Year 5 Total SDI difference of change from baseline all methods (N=567)

Method

Coefficient (SE)

[95% CI] P value

Propensity Score Matched -0.4343 (0.1188)

95% CI: [-0.6673 ; -0.2014] p<0.001

IPSW* -0.4405 (0.1163)

95% CI: [-0.6685 ; -0.2216] p<0.001

Regression Augmented IPSW -0.4499 (0.1155)

95% CI: [-0.6763 ; -0.2234] p<0.001

Abbreviations: CI, confidence interval; IPSW, inverse propensity score weighting; SDI, SLICC/ACR Damage Index; SE, standard error * Bias statistically inadequate.


The time to the first worsening (increase) in total SDI score was analyzed using parametric

survival models with a binary indicator for treatment with belimumab as the covariate. All PSM

variables were balanced (Table 15) so none were added as covariates. The baseline decade of

entry also was not significant, so it was not added as a covariate. Results for exponential,

Weibull, Gompertz, log logistic, and log normal distributions were evaluated (Table 21 to Table

25). Results indicated that belimumab was associated with a significantly lower rate of organ

damage progression, p<0.001, regardless of the distribution used.

Table 21. Proportional hazards model of time to first change in total SDI score,

exponential distribution

Variable

Regression Coefficient

Estimate (SE)

[95% CI]

Hazard Rate/Ratio

Estimate (SE)

[95% CI] p value

Intercept -2.3967 (0.1209)

95% CI: [-2.6337 ; -2.1596]

0.0910 (0.0110)

95% CI: [0.0718 ; 0.1154]

<0.001

Belimumab -0.9389 (0.2230)

95% CI: [-1.3760 ; -0.5018]

0.3911 (0.0872)

95% CI: [0.2526 ; 0.6054]

<0.001


51


distribution.

Variable


Estimate (SE)

[95% CI]

Hazard Rate/Ratio

Estimate (SE)

[95% CI] p value

Intercept -2.2616 (0.1593)

95% CI: [-2.5739 ; -1.9493]

0.1042 (0.0166)

95% CI: [0.0762 ; 0.1424]

<0.001

Belimumab -0.9651 (0.2205)

95% CI: [-1.3972 ; -0.5329]

0.3810 (0.0840)

95% CI: [0.2473 ; 0.5869]

<0.001



distribution.

Variable


Estimate (SE)

[95% CI]

Hazard Rate/Ratio

Estimate (SE)

[95% CI] p value

Intercept -2.5094 (0.1584)

95% CI: [-2.8199 ; -2.1990]

0.0813 (0.0129)

95% CI: [0.0596 ; 0.1109]

<0.001

Belimumab -0.9327 (0.2256)

95% CI: [-1.3749 ; -0.4906]

0.3935 (0.0888)

95% CI: [0.2529 ; 0.6123]

<0.001

p 1.0612 (0.06177)

95% CI: [0.9468 ; 1.1894]

NA 0.308

Abbreviations: CI, confidence interval; NA, not applicable; SDI, SLICC/ACR Damage Index; SE, standard error

Table 24. Accelerated failure time model of time to first change in total SDI score,

loglogistic distribution

Variable


Estimate (SE)

[95% CI] p value

Intercept 1.9596 (0.1342)

95% CI: 1.6966; 2.2226]

<0.001

Belimumab 0.9310 (0.2140)

95% CI: 0.5515 ; 1.3504]

<0.001

gamma 0.7881 (0.0447)

95% CI: [0.7052 ; 0.8807]

<0.001

Abbreviations: CI, confidence interval; NR, not reported; SDI, SLICC/ACR Damage Index; SE, standard error

52


lognormal distribution

Variable


Estimate (SE)

[95% CI] p value

Intercept 1.9990 (0.1353)

95% CI: 1.7338; 2.2642]

<0.001

Belimumab 0.8930 (0.1960)

95% CI: [0.5087; 1.2772]

<0.001

sigma 1.3595 (0.0734)

95% CI: [1.2230 ; 1.5112]

<0.001

Abbreviations: CI, confidence interval; SDI, SLICC/ACR Damage Index; SE, standard error; NA, could not be calculated

The information criterion scores for the models are displayed in Table 26. The lognormal

distribution produced substantially better measures of fit (lower AIC and Bayesian information

criteria [BIC] scores) than the other distributions.

Table 26. Fit of regression models of time to first change in total SDI score

Distribution AIC BIC

Exponential 596.8 604.5

Gompertz 597.4 609.0

Loglogistic 591.5 603.1

Weibull 598.2 609.9

Lognormal 582.1 593.7

Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion


The counts and proportions of subjects in each treatment arm, out to year 5, of the incremental

changes from baseline of total SDI score are displayed in Table 27. A substantial difference

occurs immediately. In the first year 17 (17.2%) SoC subjects had an increase in their total SDI,

with 7 (7.1%) subjects experiencing an increase of greater than 1. In contrast, only 3 (3.0%)

belimumab subjects had an increase and all were an increase of 1. By the fifth year only 5

(5.1%) belimumab subjects had an increase greater than 1 whereas 19 (19.2%) SoC subjects

had an increase greater than 1. Overall, by the fifth year 77 (77.8%) of belimumab subjects saw

no change, while 59 (59.6%) of SoC subjects saw no change.

53

Table 27. SDI change from baseline

Year 1 Year 2 Year 3 Year 4 Year 5

SDI

Change

SoC

N=99

Belim

N=99

SoC

N=99

Belim

N=99

SoC

N=99

Belim

N=99

SoC

N=99

Belim

N=99

SoC

N=99

Belim

N=99

0 [n (%)] 82

(82.8%) 96

(97.0%) 75

(75.8%) 87

(87.9%) 71

(71.7%) 79

(79.8%) 67

(67.7%) 78

(78.8%) 59

(59.6%) 77

(77.8%)

+1 [n (%)] 10

(10.1%) 3

(3.0%) 14

(14.1%) 11

(11.1%) 15

(15.2%) 16

(16.2%) 17

(17.2%) 17

(17.2%) 21

(21.2%) 17

(17.2%)

+2 [n (%)] 6

(6.1%) 0

8 (8.1%)

1 (1.0%)

8 (8.1%)

3 (3.0%)

9 (9.1%)

3 (3.0%)

12 (12.1%)

4 (4.0%)

+3 [n (%)] 0 0 0 0 2

(2.0%) 1

(1.0%) 2

(2.0%) 1

(1.0%) 3

(3.0%) 1

(1.0%)

+4 [n (%)] 1

(1.0%) 0

2 (2.0%)

0 3

(3.0%) 0

4 (4.0%)

0 3

(3.0%) 0

+5 [n (%)] 0 0 0 0 0 0 0 0 1

(1.0%) 0

Abbreviations: Belim, Belimumab; SDI, SLICC/ACR Damage Index; SoC, standard of care

For year 1, the constrained continuation ratio logit model provided an adequate fit (p=0.456);

allowing for the assumption of a constant odds ratio across SDI baseline change categories (0,

≥1; +1, ≥2). The type of treatment subjects received had a significant (p=0.002) effect on SDI

change from baseline (Table 28). The odds of an increase from baseline total SDI score and the

odds of having an increase greater than one given an increase were each 7.3728 (1 / 0.1356)

times greater for subjects receiving SoC versus subjects taking belimumab.

54

Table 28. SDI change from baseline constrained model year 1

Variable Coefficient (SE)

[95% CI] P value

Odds Ratio [95% CI]

Intercept:1 logit[P(ΔSDI>0|ΔSDI≥0)]

-1.5561 (0.2633) 95% CI: [-2.0722 ; -1.0401]

p<0.001 NA

Belimumab

-1.9978 (0.6387) 95% CI: [-3.2497 ; -0.7459]

p=0.002

0.1356 95% CI: [0.0388 ; 0.4743]


-0.4168 (0.4812) 95% CI: [-1.3598 ; 0.5262]

p=0.386 NA

Degrees of freedom Deviance (P value)

1 0.55 (p=0.456)

Abbreviations: ΔSDI, SDI change from baseline; CI, confidence interval; NA, not applicable; SDI, SLICC/ACR Damage Index; SE, standard error







55



[95% CI] P value

Odds Ratio [95% CI]


-1.0791 (0.2245) 95% CI: [-1.5191 ; -0.6392]

p<0.001

NA

Belimumab

-1.0119 (0.3620) 95% CI: [-1.7214 ; -0.3025]

p=0.005

0.3635 95% CI: [0.1788 ; 0.7390]


-0.5310 (0.3747) 95% CI: [-1.2654 ; 0.2034]

p=0.156

NA


1 1.26 (p=0.262)








56



[95% CI] P value

Odds Ratio [95% CI]


-0.8574 (0.2094) 95% CI: [-1.2679 ; -0.4469]

p<0.001 NA

Belimumab

-0.6124 (0.2995) 95% CI: [-1.1995 ; -0.0253]

p=0.041

0.5420 95% CI: [0.3014 ; 0.9750]


-0.3595 (0.3206) 95% CI: [-0.9879 ; 0.2688]

p=0.262 NA


1 1.17 (p=0.279)








57



[95% CI] P value

Odds Ratio [95% CI]


-0.6718 (0.2026) 95% CI: [-1.0688 ; -0.2747]

p<0.001 NA

Belimumab

-0.7317 (0.2917) 95% CI: [-1.3033 ; -0.1601]

p=0.012

0.4811 95% CI: [0.2716 ; 0.8521]


-0.3114 (0.3038) 95% CI: [-0.9068 ; 0.2840]

p=0.305 NA


1 1.08 (p=0.298)




≥1; +1, ≥2). The type of treatment subjects received had a significant (p<0.001) effect on SDI




58



[95% CI] P value

Odds Ratio [95% CI]


-0.3645 (0.1946) 95% CI: [-0.7459 ; 0.0168]

p=0.061

NA

Belimumab

-0.9222 (0.2799) 95% CI: [-1.4709 ; -0.3735]

p<0.001

0.3976 95% CI: [0.2297 ; 0.6883]


-0.1580 (0.2778) 95% CI: [-0.7025 ; 0.3866]

p=0.570

NA


1 0.15 (p=0.700)



The change of total SDI score from baseline to end of years 1 through 5 was analyzed using

linear regression with a binary indicator for treatment with belimumab as a covariate. All PSM

variables were balanced (Table 11) so none were added as covariates.

The baseline decade of entry was initially included as a covariate in the year 1 analysis, but it

was not statistically significant. In fact, baseline decade of entry was not a significant factor for

any of the five years from baseline. The results from the regression with baseline decade of

entry as a covariate were omitted for subsequent years.

When baseline decade of entry was included as a covariate the difference from baseline in the

first year total SDI score was significantly (p=0.002) lower for subjects taking belimumab. See

Table 33.

59

Table 33. Year 1 Total SDI difference of change from baseline controlled for entry decade

Variable


[95% CI] P value


[95% CI] P value

Intercept 0.2000 (0.0965)

95% CI: [0.0097 ; 0.3903] p=0.040

0.2000 (0.0992) 95% CI: [0.0055 ; 0.3945]

p=0.044

Belimumab -0.2801 (0.0755)

95% CI: [-0.4290 ; -0.1311] p<0.001

-0.2801 (0.0902) 95% CI: [-0.4569 ; -0.1032]

p=0.002

Entry Decade 2000 0.1203 (0.1133)

95% CI: [-0.1032 ; 0.3438] p=0.290

0.1203 (0.1352) 95% CI: [-0.1446 ; 0.3852]

p=0.374

Entry Decade 2010 -0.1253 (0.1589)

95% CI: [-0.4386 ; 0.1880] p=0.431

-0.1253 (0.1075) 95% CI: [-0.3360 ; 0.0854]

p=0.244


Without controlling for baseline decade of entry the results were similar. The change in total SDI

score from baseline at the end of the first year was significantly (p<0.001) lower for subjects

taking belimumab. See Table 34. The average SDI change from baseline was lower by 0.2323

for subjects taking belimumab compared to those receiving SoC.


Variable


[95% CI] P value


[95% CI] P value

Intercept 0.2626 (0.0487)

95% CI: [0.1665 ; 0.3587] p<0.001

0.2626 (0.0669) 95% CI: [0.1315 ; 0.3937]

p<0.001

Belimumab -0.2323 (0.0689)

95% CI: [-0.3682 ; -0.0964] p<0.001

-0.2323 (0.0688) 95% CI: [-0.3671 ; -0.0975]

p<0.001


For years 2 through 5 the change in total SDI score from baseline was always significantly lower

for belimumab. See Table 35 to Table 38. After two years the average SDI change from

60

baseline was lower by 0.2525 for subjects taking belimumab compared to those receiving SoC.

The magnitude of the difference decreased slightly in year 3 to 0.2424. This was followed by

subsequent substantial increases in the last two years. By the end of year 4 belimumab subjects

had an average difference that was lower by 0.3131. In the last year the belimumab average

change from baseline was 0.4343 less.


Variable


[95% CI] P value


[95% CI] P value

Intercept 0.3838 (0.0629)

95% CI: [0.2599 ; 0.5078] p<0.001

0.3838 (0.0811) 95% CI: [0.2250 ; 0.5427]

p<0.001

Belimumab -0.2525 (0.0889)

95% CI: [-0.4279 ; -0.0772] p=0.005

-0.2525 (0.0806) 95% CI: [-0.4105 ; -0.0946]

p=0.002



Variable


[95% CI] P value


[95% CI] P value

Intercept 0.4949 (0.0785)

95% CI: [0.3402 ; 0.6497] p<0.001

0.4949 (0.0959) 95% CI: [0.3070 ; 0.6829]

p<0.001

Belimumab -0.2424 (0.1110)

95% CI: [-0.4612 ; -0.0236] p=0.030

-0.2424 (0.1078) 95% CI: [-0.4537 ; -0.0311]

p=0.025


61


Variable


[95% CI] P value


[95% CI] P value

Intercept 0.5758 (0.0829)

95% CI: [0.4123 ; 0.7393] p<0.001

0.5758 (0.1029) 95% CI: [0.3741 ; 0.7774]

p<0.001

Belimumab -0.3131 (0.1172)

95% CI: [-0.5442 ; -0.0820] p=0.008

-0.3131 (0.1166) 95% CI: [-0.5417 ; -0.0845]

p=0.007



Variable


[95% CI] P value


[95% CI] P value

Intercept 0.7172 (0.0886)

95% CI: [0.5425 ; 0.8918] p<0.001

0.7172 (0.1106) 95% CI: [0.5004 ; 0.9339]

p<0.001

Belimumab -0.4343 (0.1252)

95% CI: [-0.6813 ; -0.1874] p<0.001

-0.4343 (0.1188) 95% CI: [-0.6673 ; -0.2014]

p<0.001



The annual transition probability of a change in SDI was estimated by combining the results

from the time to first SDI worsening analysis (6.1.3) with the observed conditional probability

that the increase in SDI score was 1 point versus 2+ points. The resulting annual probabilities

are shown in

Table 39. A constant hazard was assumed and time to first SDI worsening was modeled using

an exponential distribution (Table 21). The conditional probabilities were derived separately

using the observed counts for the specific treatment arm.

62

Table 39. Annual transition probabilities

SoC Belimumab

No SDI change 0.9130 0.9650

SDI increase by 1 0.0604 0.0329


Abbreviations: SoC, standard of care; SDI, SLICC/ACR Damage Index


The counts and proportions of the incremental changes from baseline out to year 5 for each of

the SDI organ system subscores are displayed in the even numbered tables of Section 6.1.7.

Results of two-sided Fisher’s exact tests for each SDI organ system subscore are displayed in

the odd numbered tables for each year, also in Section 6.1.7.

The majority of the subscores showed no significant difference in the proportion of subjects with

a change from baseline at the end of year 5 or any prior year; the exceptions were

musculoskeletal and skin subscores.

At the end of the fifth year 11 (11.1%) SoC subjects had seen an increase from their baseline

ocular system subscore. See Table 40. In comparison, only 4 (4.0%) of belimumab subjects had

seen an increase. No subjects taking belimumab had an increase in the first two years.

Table 40. SDI ocular system subscore change from baseline by year

SDI

Ocular Year 1 Year 2 Year 3 Year 4 Year 5

Change

from Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 97

(98.0%) 99

(100.0%) 95

(96.0%) 99

(100.0%) 93

(93.9%) 96

(97.0%) 92

(92.9%) 96

(97.0%) 88

(88.9%) 95

(96.0%)

+1 [n (%)] 2

(2.0%) 0

3 (3.0%)

0 5

(5.1%) 3

(3.0%) 6

(6.1%) 3

(3.0%) 10

(10.1%) 4

(4.0%)

+2 [n (%)] 0 0 1

(1.0%) 0

1 (1.0%)

0 1

(1.0%) 0

1 (1.0%)

0

Abbreviations: Belim, belimumab; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the ocular system subscore there was no significant difference in the proportion of subjects

with an increase from their baseline score by the end of the 5th year (p=0.104) or at the end of

any of the prior years. See Table 41.

63

Table 41. Fisher’s test for belimumab versus SoC SDI ocular subscore change from

baseline

YEAR Odds Ratio 95% CI P value

1 0 0 ; 5.316 0.497

2 0 0 ; 1.496 0.121

3 0.486 0.076 ; 2.356 0.498

4 0.412 0.067 ; 1.874 0.331

5 0.339 0.076 ; 1.196 0.104

Abbreviations: CI, confidence interval; SDI, SLICC/ACR Damage Index; SoC, standard of care


neuropsychiatric system subscore. See Table 42. For belimumab subjects, 7 (7.1%) had seen

an increase. The difference in the number of SoC subjects compared to belimumab subjects

experiencing any increase remained fairly constant throughout the five years.

Table 42. SDI neuropsychiatric system subscore change from baseline by year

SDI

Neuro Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 97

(98.0%) 98

(99.0%) 96

(97.0%) 94

(94.9%) 93

(93.9%) 92

(92.9%) 93

(93.9%) 92

(92.9%) 91

(91.9%) 92

(92.9%)

+1 [n (%)] 2

(2.0%) 1

(1.0%) 3

(3.0%) 5

(5.1%) 6

(6.1%) 6

(6.1%) 6

(6.1%) 6

(6.1%) 8

(8.1%) 6

(6.1%)

+2 [n (%)] 0 0 0 0 0 1

(1.0%) 0

1 (1.0%)

0 1

(1.0%)

Abbreviations: Belim, belimumab; Nuero, neuropsychiatric; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the neuropsychiatric system subscore there was no significant difference in the proportion of

subjects with an increase from their baseline score by the end of the 5th year (p=1.000) or at

the end of any of the prior years. See Table 43.

64


change from baseline


1 0.497 0.008 ; 9.685 1.000

2 1.698 0.320 ; 11.241 0.721

3 1.178 0.325 ; 4.418 1.000

4 1.178 0.325 ; 4.418 1.000

5 0.866 0.256 ; 2.860 1.000


At the end of the fifth year 2 (2.0%) SoC subjects had seen an increase from their baseline renal

system subscore, whereas, no belimumab subjects saw an increase. See Table 44. The

difference in the number of SoC subjects compared to belimumab subjects experiencing any

increase remained constant throughout the five years with the only SoC changes coming in the

first year.

Table 44. SDI renal system subscore change from baseline by year

SDI

Renal Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 97

(98.0%) 99

(100.0%) 97

(98.0%) 99

(100.0%) 97

(98.0%) 99

(100.0%) 97

(98.0%) 99

(100.0%) 97

(98.0%) 99

(100.0%)

+1 [n (%)] 2

(2.0%) 0

2 (2.0%)

0 2

(2.0%) 0

2 (2.0%)

0 2

(2.0%) 0


For the renal system subscore there was no significant difference in the proportion of subjects



65

Table 45. Fisher’s test for belimumab versus SoC SDI renal system subscore change

from baseline


1 0 0 ; 5.316 0.497

2 0 0 ; 5.316 0.497

3 0 0 ; 5.316 0.497

4 0 0 ; 5.316 0.497

5 0 0 ; 5.316 0.497



pulmonary system subscore, whereas, no belimumab subjects saw an increase. See Table 46.

One SoC subject saw an increase each year except in year 4.

Table 46. SDI pulmonary system subscore change from baseline by year

SDI

Pulmonary Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 98

(99.0%) 99

(100.0%) 97

(98.0%) 99

(100.0%) 97

(98.0%) 99

(100.0%) 97

(98.0%) 99

(100.0%) 96

(97.0%) 99

(100.0%)

+1 [n (%)] 1

(1.0%) 0

2 (2.0%)

0 1

(1.0%) 0

1 (1.0%)

0 2

(2.0%) 0

+2 [n (%)] 0 0 0 0 1

(1.0%) 0

1 (1.0%)

0 1

(1.0%) 0


For the pulmonary system subscore there was no significant difference in the proportion of



66

Table 47. Fisher’s test for belimumab versus SoC SDI pulmonary system subscore



1 0 0 ; 39.001 1.000

2 0 0 ; 5.316 0.497

3 0 0 ; 5.316 0.497

4 0 0 ; 5.316 0.497

5 0 0 ; 2.405 0.246



cardiovascular system subscore. See Table 48. For belimumab subjects, 5 (5.1%) had seen an

increase. The difference in the number of SoC subjects compared to belimumab subjects

experiencing any increase remained fairly constant throughout the five years.

Table 48. SDI cardiovascular system subscore change from baseline by year

SDI

CV Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 97

(98.0%) 99

(100.0%) 97

(98.0%) 97

(98.0%) 96

(97.0%) 95

(96.0%) 94

(94.9%) 95

(96.0%) 93

(93.9%) 94

(94.9%)

+1 [n (%)] 2

(2.0%) 0

2 (2.0%)

1 (1.0%)

2 (2.0%)

3 (3.0%)

4 (4.0%)

3 (3.0%)

5 (5.1%)

4 (4.0%)

+2 [n (%)] 0 0 0 1

(1.0%) 0

1 (1.0%)

0 1

(1.0%) 0

1 (1.0%)

+3 [n (%)] 0 0 0 0 1

(0.0%) 0

1 (0.0%)

0 1

(0.0%) 0

Abbreviations: Belim, Belimumab; CV, cardiovascular; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the cardiovascular system subscore there was no significant difference in the proportion of



67

Table 49. Fisher’s test for belimumab versus SoC SDI CV subscore change from baseline


1 0 0 ; 5.316 0.497

2 1.000 0.071 ; 14.049 1.000

3 1.345 0.221 ; 9.429 1.000

4 0.793 0.152 ; 3.808 1.000

5 0.825 0.192 ; 3.371 1.000

Abbreviations: CI, confidence interval; CV, cardiovascular; SDI, SLICC/ACR Damage Index; SoC, standard of care


peripheral vascular system subscore. See Table 50. For belimumab subjects, 2 (2.0%) had

seen an increase. No subject saw an increase in the first year.

Table 50. SDI peripheral vascular system subscore change from baseline by year

SDI

PV Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 99

(100.0%) 99

(100.0%) 99

(100.0%) 98

(99.0%) 99

(100.0%) 97

(98.0%) 98

(99.0%) 97

(98.0%) 98

(99.0%) 97

(98.0%)

+1 [n (%)] 0 0 0 1

(1.0%) 0

1 (1.0%)

1 (1.0%)

1 (1.0%)

1 (1.0%)

1 (1.0%)

+2 [n (%)] 0 0 0 0 0 1

(1.0%) 0

1 (1.0%)

0 1

(1.0%)

Abbreviations: Belim, Belimumab; PV, peripheral vascular; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the peripheral vascular system subscore there was no significant difference in the

proportion of subjects with an increase from their baseline score by the end of the 5th year

(p=1.000) or at the end of any of the prior years. See Table 51.

68

Table 51. Fisher’s test for belimumab versus SoC SDI peripheral vascular system

subscore change from baseline


1 0 0 ; Inf 1.000

2 Inf 0.026 ; Inf 1.000

3 Inf 0.188 ; Inf 0.497

4 2.014 0.103 ; 120.322 1.000

5 2.014 0.103 ; 120.322 1.000

Abbreviations: CI, confidence interval; Inf, infinity; SDI, SLICC/ACR Damage Index; SoC, standard of care


gastrointestinal system subscore. See Table 52. For belimumab subjects, 2 (2.0%) had seen an

increase. No changes occurred after the second year.

Table 52. SDI gastrointestinal system subscore change from baseline by year

SDI

GI Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 99

(100.0%) 98

(99.0%) 98

(99.0%) 97

(98.0%) 98

(99.0%) 97

(98.0%) 98

(99.0%) 97

(98.0%) 98

(99.0%) 97

(98.0%)

+1 [n (%)] 0 1

(1.0%) 1

(1.0%) 2

(2.0%) 1

(1.0%) 2

(2.0%) 1

(1.0%) 2

(2.0%) 1

(1.0%) 2

(2.0%)

Abbreviations: Belim, belimumab; GI, gastrointestinal; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the gastrointestinal system subscore there was no significant difference in the proportion of






1 Inf 0.026 ; Inf 1.000

2 2.014 0.103 ; 120.322 1.000

3 2.014 0.103 ; 120.322 1.000

4 2.014 0.103 ; 120.322 1.000

5 2.014 0.103 ; 120.322 1.000


69


musculoskeletal system subscore, with 7 of those being an increase greater than one. See

Table 54. In comparison, only 3 (3.0%) belimumab subjects had seen an increase with none

being greater than 1. The first year saw the largest number (10) of SoC subjects experiencing

an increase. The difference in the number of SoC subjects compared to belimumab subjects

experiencing any increase grew or remained the same from year to year.

Table 54. SDI musculoskeletal system subscore change from baseline by year

SDI

MS Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 89

(89.9%) 98

(99.0%) 88

(88.9%) 97

(98.0%) 86

(86.9%) 96

(97.0%) 85

(85.9%) 96

(97.0%) 83

(83.8%) 96

(97.0%)

+1 [n (%)] 8

(8.1%) 1

(1.0%) 7

(7.1%) 2

(2.0%) 9

(9.1%) 3

(3.0%) 9

(9.1%) 3

(3.0%) 9

(9.1%) 3

(3.0%)

+2 [n (%)] 2

(2.0%) 0

4 (4.0%)

0 4

(4.0%) 0

4 (4.0%)

0 6

(6.1%) 0

+3 [n (%)] 0 0 0 0 0 0 1

(0.0%) 0

1 (0.0%)

0

Abbreviations: Belim, belimumab; MS, musculoskeletal; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the musculoskeletal system subscore the odds of belimumab subjects experiencing an

increase from their baseline score by the end of the 5th year were significantly lower compared

to SoC subjects (p=0.003). In fact, for belimumab subjects, the odds were significantly less in

the first year (p=0.010) and continued to be significantly less for the intervening years. See

Table 55.




1 0.092 0.002 ; 0.667 0.010

2 0.166 0.017 ; 0.793 0.018

3 0.208 0.037 ; 0.793 0.016

4 0.191 0.034 ; 0.718 0.009

5 0.163 0.030 ; 0.599 0.003


70

At the end of the fifth year 8 (8.1%) SoC subjects had seen an increase from their baseline skin

system subscore, with no increase for any belimumab subject. See Table 56. The first year saw

the largest number (4) of SoC subjects experiencing an increase. Except for the third year, SoC

subjects saw an increase each year.

Table 56. SDI skin system subscore change from baseline by year

SDI

Skin Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 95

(96.0%) 99

(100.0%) 93

(93.9%) 99

(100.0%) 93

(93.9%) 99

(100.0%) 92

(92.9%) 99

(100.0%) 91

(91.9%) 99

(100.0%)

+1 [n (%)] 4

(4.0%) 0

6 (6.1%)

0 6

(6.1%) 0

7 (7.1%)

0 7

(7.1%) 0

+2 [n (%)] 0 0 0 0 0 0 0 0 1

(1.0%) 0


For the skin system subscore the odds of belimumab subjects experiencing an increase from

their baseline score by the end of the 5th year were significantly lower compared to SoC

subjects (p=0.007). In fact, for belimumab subjects, the odds were significantly less for all but

the first year. See Table 57.

Table 57. Fisher’s test for belimumab versus SoC SDI skin subscore change from

baseline


1 0 0 ; 1.496 0.121

2 0 0 ; 0.826 0.029

3 0 0 ; 0.826 0.029

4 0 0 ; 0.668 0.014

5 0 0 ; 0.559 0.007

Abbreviations: CI, confidence interval; SDI, SLICC/ACR Damage Index; SoC, Standard of Care

At the end of the fifth year 1 (1.0%) SoC subject had seen an increase from their baseline

premature gonadal failure subscore, whereas, no belimumab subjects saw an increase. See

Table 58. The one SoC subject who saw an increase did so in the first year.

71

Table 58. SDI premature gonadal failure subscore change from baseline by year

SDI

Gonadal Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 98

(99.0%) 99

(100.0%) 98

(99.0%) 99

(100.0%) 98

(99.0%) 99

(100.0%) 98

(99.0%) 99

(100.0%) 98

(99.0%) 99

(100.0%)

+1 [n (%)] 1

(1.0%) 0

1 (1.0%)

0 1

(1.0%) 0

1 (1.0%)

0 1

(1.0%) 0


For the premature gonadal failure subscore there was no significant difference in the proportion

of subjects with an increase from their baseline score by the end of the 5th year (p=1.000) or at


Table 59. Fisher’s test for belimumab versus SoC SDI premature gonadal failure



1 0 0 ; 39.001 1.000

2 0 0 ; 39.001 1.000

3 0 0 ; 39.001 1.000

4 0 0 ; 39.001 1.000

5 0 0 ; 39.001 1.000


At the end of the fifth year 1 (1.0%) SoC subject had seen an increase from their baseline

diabetes subscore. See Table 60. For belimumab subjects, 2 (2.0%) had seen an increase.

Table 60. SDI diabetes subscore change from baseline by year

SDI

Diabetes Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 99

(100.0%) 99

(100.0%) 98

(99.0%) 99

(100.0%) 98

(99.0%) 98

(99.0%) 98

(99.0%) 97

(98.0%) 98

(99.0%) 97

(98.0%)

+1 [n (%)] 0 0 1

(1.0%) 0

1 (1.0%)

1 (1.0%)

1 (1.0%)

2 (2.0%)

1 (1.0%)

2 (2.0%)


72

For the diabetes subscore there was no significant difference in the proportion of subjects with

an increase from their baseline score by the end of the 5th year (p=1.000) or at the end of any

of the prior years, See Table 61.


baseline


1 0 0 ; Inf 1.000

2 0 0 ; 39.001 1.000

3 1.000 0.013 ; 79.241 1.000

4 2.014 0.103 ; 120.322 1.000

5 2.014 0.103 ; 120.322 1.000


No subject in either treatment arm saw an increase in their malignancy subscore.

Table 62. SDI malignancy subscore change from baseline by year

SDI

Malig Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

SoC N=99

Belim N=99

0 [n (%)] 99

(100.0%) 99

(100.0%) 99

(100.0%) 99

(100.0%) 99

(100.0%) 99

(100.0%) 99

(100.0%) 99

(100.0%) 99

(100.0%) 99

(100.0%)

Abbreviations: Belim, belimumab; Malig, malignancy; SDI, SLICC/ACR Damage Index; SoC, standard of care


subscores

The total change from baseline for SDI subscores was analyzed using linear regression with the

difference between the subject’s score in their final year and their baseline score used as the

response variable. An indicator variable for treatment with belimumab along with a categorical

variable for the decade of entry were included as covariates. The year from baseline was also

included to control for the length of time from baseline. All PSM variables were balanced so

none were added as covariates.

The data set for this analysis consisted of the 358 PS matched patients where subjects were not

restricted to at least 5 years of follow-up. A second analysis was performed using the 5th year of

the primary 198 PS matched patients. The results from this analysis were used to check the

robustness of the results where subjects’ scores were recorded in different years.

73

The majority of the subscores showed no significant difference by treatment arm in the change

between the subject’s score in their final year and their baseline score; the exceptions were

musculoskeletal and skin subscores.

If baseline decade was not a significant factor in the SDI subscore change from baseline the

results were omitted for models with baseline decade as an explanatory variable.

For the cardiovascular SDI system subscore there was no significant difference (p=0.502)

between belimumab and SoC in the change from baseline score. See Table 63.

Table 63. SDI cardiovascular system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value

PS matched Year 5 Coefficient (SE)

[95% CI] P value

Intercept -0.0501 (0.0392)

95% CI: [-0.1272 ; 0.0270] p=0.202

-0.0501 (0.0317) 95% CI: [-0.1122 ; 0.0120]

p=0.114

0.0808 (0.0329) 95% CI: [0.0159 ; 0.1457]

p=0.015

Belimumab -0.0249 (0.0370)

95% CI: [-0.0977 ; 0.0479] p=0.502

-0.0249 (0.0299) 95% CI: [-0.0835 ; 0.0337]

p=0.405

-0.0202 (0.0465) 95% CI: [-0.1119 ; 0.0715]

p=0.665

Final Year 0.0201 (0.0037)

95% CI: [0.0128 ; 0.0274] p<0.001

0.0201 (0.0064) 95% CI: [0.0076 ; 0.0326]

p=0.002

NA

Abbreviations: CI, confidence interval; NA, not applicable; OLS, ordinary least squares; PS, propensity score; SDI, SLICC/ACR Damage Index; SE, standard error

When controlling for decade of entry, for the SDI diabetes subscore there was no significant

difference (p=0.138) between belimumab and SoC in the change from baseline score. See

Table 64.

74

Table 64. SDI diabetes subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0394 (0.0295)

95% CI: [-0.0186 ; 0.0974] p=0.182

0.0394 (0.0348) 95% CI: [-0.0288 ; 0.1077]

p=0.257

0.0000 (0.0246) 95% CI: [-0.0486 ; 0.0486]

p=1.000

Belimumab 0.0303 (0.0204)

95% CI: [-0.0098 ; 0.0704] p=0.138

0.0303 (0.0138) 95% CI: [0.0032 ; 0.0574]

p=0.029

0.0048 (0.0193) 95% CI: [-0.0332 ; 0.0428]

p=0.803

Final Year 0.0032 (0.0018)

95% CI: [-0.0004 ; 0.0068] p=0.081

0.0032 (0.0023) 95% CI: [-0.0013 ; 0.0077]

p=0.165 NA

Entry Decade

2000

-0.0659 (0.0269) 95% CI: [-0.1188 ; -0.0130]

p=0.015

-0.0659 (0.0346) 95% CI: [-0.1338 ; 0.0020]

p=0.057

0.0161 (0.0289) 95% CI: [-0.0410 ; 0.0732]

p=0.579

Entry Decade

2010

-0.0533 (0.0311) 95% CI: [-0.1145 ; 0.0079]

p=0.088

-0.0533 (0.0332) 95% CI: [-0.1183 ; 0.0117]

p=0.108

-0.0013 (0.0406) 95% CI: [-0.0813 ; 0.0787]

p=0.975


For the SDI gastrointestinal system subscore there was no significant difference (p=0.675)

between belimumab and SoC in the change from baseline score, See Table 65.

Table 65. SDI gastrointestinal system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept -0.0031 (0.0148)

95% CI: [-0.0322 ; 0.0259] p=0.832

-0.0031 (0.0129) 95% CI: [-0.0284 ; 0.0222]

p=0.808

0.0101 (0.0123) 95% CI: [-0.0142 ; 0.0344]

p=0.414

Belimumab 0.0059 (0.0140)

95% CI: [-0.0216 ; 0.0333] p=0.675

0.0059 (0.0127) 95% CI: [-0.0190 ; 0.0308]

p=0.644

0.0101 (0.0174) 95% CI: [-0.0243 ; 0.0445]

p=0.563

Final Year 0.0025 (0.0014)

95% CI: [-0.0003 ; 0.0052] p=0.078

0.0025 (0.0020) 95% CI: [-0.0015 ; 0.0064]

p=0.222 NA


75

When controlling for decade of entry, for the SDI premature gonadal failure subscore there was

no significant difference (p=0.318) between belimumab and SoC in the change from baseline

score. See Table 66.

Table 66. SDI premature gonadal failure subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0015 (0.0107)

95% CI: [-0.0195 ; 0.0225] p=0.890

0.0015 (0.0017) 95% CI: [-0.0018 ; 0.0048]

p=0.383

0.0000 (0.0143) 95% CI: [-0.0281 ; 0.0281]

p=1.000

Belimumab -0.0133 (0.0074)

95% CI: [-0.0278 ; 0.0012] p=0.073

-0.0133 (0.0133) 95% CI: [-0.0393 ; 0.0128]

p=0.318

-0.0147 (0.0112) 95% CI: [-0.0367 ; 0.0073]

p=0.188

Final Year -0.0001 (0.0007)

95% CI: [-0.0014 ; 0.0012] p=0.849

-0.0001 (0.0001) 95% CI: [-0.0004 ; 0.0002]

p=0.383 NA

Entry Decade

2000

0.0129 (0.0097) 95% CI: [-0.0062 ; 0.0321]

p=0.185

0.0129 (0.0129) 95% CI: [-0.0125 ; 0.0383]

p=0.318

0.0152 (0.0167) 95% CI: [-0.0178 ; 0.0482]

p=0.365

Entry Decade

2010

0.0002 (0.0113) 95% CI: [-0.0219 ; 0.0223]

p=0.985

0.0002 (0.0008) 95% CI: [-0.0013 ; 0.0018]

p=0.780

0.0039 (0.0235) 95% CI: [-0.0423 ; 0.0502]

p=0.867


For the SDI malignancy subscore there was no significant difference (p=0.776) between

belimumab and SoC in the change from baseline score. See Table 67.

76

Table 67. SDI malignancy subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value

Intercept -0.0102 (0.0120)

95% CI: [-0.0338 ; 0.0134] p=0.397

-0.0102 (0.0096) 95% CI: [-0.0290 ; 0.0087]

p=0.290

Belimumab -0.0032 (0.0113)

95% CI: [-0.0255 ; 0.0191] p=0.776

-0.0032 (0.0074) 95% CI: [-0.0178 ; 0.0113]

p=0.664

Final Year 0.0033 (0.0011)

95% CI: [0.0011 ; 0.0056] p=0.003

0.0033 (0.0022) 95% CI: [-0.0009 ; 0.0076]

p=0.124

Abbreviations: CI, confidence interval; OLS, ordinary least squares; PS, propensity score; SDI, SLICC/ACR Damage Index; SE, standard error

When controlling for decade of entry, for the SDI musculoskeletal system subscore belimumab

subjects had a significantly smaller (p=0.006) increase from baseline score compared to SoC

subjects. See Table 68. The treatment coefficient (-0.2424) for the 5th year change from

baseline is within the 95% confidence interval (-0.3748 ; -0.0627) of the treatment coefficient

determined when using a subject’s last visit.

77

Table 68. SDI musculoskeletal system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.3645 (0.0964)

95% CI: [0.1750 ; 0.5541] p<0.001

0.3645 (0.1384) 95% CI: [0.0932 ; 0.6359]

p=0.008

0.2000 (0.0894) 95% CI: [0.0237 ; 0.3763]

p=0.026

Belimumab -0.2188 (0.0666)

95% CI: [-0.3498 ; -0.0877] p=0.001

-0.2188 (0.0796) 95% CI: [-0.3748 ; -0.0627]

p=0.006

-0.2424 (0.0700) 95% CI: [-0.3804 ; -0.1044]

p<0.001

Final Year 0.0148 (0.0060)

95% CI: [0.0031 ; 0.0265] p=0.013

0.0148 (0.0097) 95% CI: [-0.0043 ; 0.0338]

p=0.128 NA

Entry Decade

2000

-0.1967 (0.0879) 95% CI: [-0.3696 ; -0.0238]

p=0.026

-0.1967 (0.1387) 95% CI: [-0.4685 ; 0.0751]

p=0.156

0.0787 (0.1050) 95% CI: [-0.1284 ; 0.2857]

p=0.455

Entry Decade

2010

-0.3470 (0.1017) 95% CI: [-0.5470 ; -0.1470]

p<0.001

-0.3470 (0.1265) 95% CI: [-0.5949 ; -0.0991]

p=0.006

-0.0687 (0.1471) 95% CI: [-0.3589 ; 0.2215]

p=0.641


For the SDI neuropsychiatric system subscore there was no significant difference (p=0.494)


Table 69. SDI neuropsychiatric system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept -0.0028 (0.0367)

95% CI: [-0.0750 ; 0.0694] p=0.939

-0.0028 (0.0351) 95% CI: [-0.0716 ; 0.0661]

p=0.937

0.0808 (0.0293) 95% CI: [0.0229 ; 0.1387]

p=0.006

Belimumab -0.0237 (0.0346)

95% CI: [-0.0919 ; 0.0444] p=0.494

-0.0237 (0.0295) 95% CI: [-0.0815 ; 0.0341]

p=0.421

0.0000 (0.0415) 95% CI: [-0.0818 ; 0.0818]

p=1.000

Final Year 0.0135 (0.0035)

95% CI: [0.0067 ; 0.0204] p<0.001

0.0135 (0.0057) 95% CI: [0.0024 ; 0.0247]

p=0.017

NA


78

For the SDI ocular system subscore there was no significant difference (p=0.339) between


Table 70. SDI ocular system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept -0.0217 (0.0306)

95% CI: [-0.0820 ; 0.0386] p=0.479

-0.0217 (0.0275) 95% CI: [-0.0755 ; 0.0322]

p=0.430

0.1212 (0.0291) 95% CI: [0.0639 ; 0.1785]

p<0.001

Belimumab -0.0277 (0.0289)

95% CI: [-0.0846 ; 0.0292] p=0.339

-0.0277 (0.0252) 95% CI: [-0.0771 ; 0.0217]

p=0.272

-0.0808 (0.0411) 95% CI: [-0.1618 ; 0.0002]

p=0.051

Final Year 0.0166 (0.0029)

95% CI: [0.0109 ; 0.0223] p<0.001

0.0166 (0.0045) 95% CI: [0.0078 ; 0.0253]

p<0.001

NA


When controlling for decade of entry, for the SDI peripheral vascular system subscore there was



79

Table 71. SDI peripheral vascular system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.1223 (0.0432)

95% CI: [0.0373 ; 0.2073] p=0.005

0.1223 (0.0747) 95% CI: [-0.0240 ; 0.2686]

p=0.101

0.0000 (0.0349) 95% CI: [-0.0687 ; 0.0687]

p=1.000

Belimumab -0.0119 (0.0299)

95% CI: [-0.0706 ; 0.0469] p=0.692

-0.0119 (0.0248) 95% CI: [-0.0605 ; 0.0368]

p=0.633

0.0146 (0.0273) 95% CI: [-0.0392 ; 0.0684]

p=0.594

Final Year -0.0006 (0.0027)

95% CI: [-0.0058 ; 0.0047] p=0.827

-0.0006 (0.0037) 95% CI: [-0.0078 ; 0.0066]

p=0.874 NA

Entry Decade

2000

-0.0893 (0.0394) 95% CI: [-0.1669 ; -0.0118]

p=0.024

-0.0893 (0.0632) 95% CI: [-0.2133 ; 0.0346]

p=0.158

0.0166 (0.0409) 95% CI: [-0.0642 ; 0.0973]

p=0.686

Entry Decade

2010

-0.1191 (0.0456) 95% CI: [-0.2088 ; -0.0295]

p=0.009

-0.1191 (0.0671) 95% CI: [-0.2507 ; 0.0124]

p=0.076

-0.0039 (0.0574) 95% CI: [-0.1170 ; 0.1093]

p=0.946


For the SDI pulmonary system subscore there was no significant difference (p=0.114) between


Table 72. SDI pulmonary system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0219 (0.0190)

95% CI: [-0.0155 ; 0.0594] p=0.250

0.0219 (0.0231) 95% CI: [-0.0233 ; 0.0672]

p=0.343

0.0404 (0.0174) 95% CI: [0.0062 ; 0.0746]

p=0.021

Belimumab -0.0285 (0.0180)

95% CI: [-0.0638 ; 0.0069] p=0.114

-0.0285 (0.0167) 95% CI: [-0.0612 ; 0.0043]

p=0.088

-0.0404 (0.0245) 95% CI: [-0.0888 ; 0.0080]

p=0.101

Final Year 0.0021 (0.0018)

95% CI: [-0.0014 ; 0.0057] p=0.236

0.0021 (0.0027) 95% CI: [-0.0032 ; 0.0075]

p=0.437

NA


80

For the SDI renal system subscore there was a marginally significant difference (p=0.084)

between belimumab and SoC in the change from baseline score. See Table 73. Subjects taking

belimumab tended to see a smaller increase from their baseline score. The treatment coefficient

(-0.0202) for the 5th year change from baseline is within the 95% confidence interval (-0.0720 ;

0.0046) of the treatment coefficient determined when using a subject’s last visit.

Table 73. SDI renal system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0131 (0.0206)

95% CI: [-0.0274 ; 0.0537] p=0.525

0.0131 (0.0278) 95% CI: [-0.0413 ; 0.0675]

p=0.636

0.0202 (0.0101) 95% CI: [0.0004 ; 0.0400]

p=0.046

Belimumab -0.0337 (0.0195)

95% CI: [-0.0720 ; 0.0046] p=0.084

-0.0337 (0.0189) 95% CI: [-0.0708 ; 0.0033]

p=0.074

-0.0202 (0.0142) 95% CI: [-0.0482 ; 0.0078]

p=0.157

Final Year 0.0046 (0.0019)

95% CI: [0.0008 ; 0.0084] p=0.018

0.0046 (0.0032) 95% CI: [-0.0017 ; 0.0109]

p=0.150

NA


For the SDI skin system subscore belimumab subjects had a significantly smaller (p=0.029)

increase from baseline score compared to SoC subjects. See Table 74. The treatment

coefficient (-0.0909) for the 5th year change from baseline is within the 95% confidence interval

(-0.0921 ; -0.0051) of the treatment coefficient determined when using a subject’s last visit.

81

Table 74. SDI skin system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0045 (0.0234)

95% CI: [-0.0416 ; 0.0506] p=0.847

0.0045 (0.0352) 95% CI: [-0.0644 ; 0.0735]

p=0.898

0.0909 (0.0229) 95% CI: [0.0457 ; 0.1361]

p<0.001

Belimumab -0.0486 (0.0221)

95% CI: [-0.0921 ; -0.0051] p=0.029

-0.0486 (0.0188) 95% CI: [-0.0855 ; -0.0117]

p=0.010

-0.0909 (0.0324) 95% CI: [-0.1548 ; -0.0270]

p=0.006

Final Year 0.0078 (0.0022)

95% CI: [0.0034 ; 0.0121] p<0.001

0.0078 (0.0050) 95% CI: [-0.0021 ; 0.0176]

p=0.122

NA



The 5-year adjusted mean SLEDAI (AMS) was analyzed using linear regression with a binary

indicator for treatment with belimumab as a covariate. All PSM variables were balanced (Table

11) so none were added as covariates. The decade of entry was initially included as a covariate

(Table 75), but was not statistically significant (p=0.118 and p= 0.066 for 2000 and 2010,

respectively).

Controlling for decade of entry there was no significant difference (p=0.892) in 5-year AMS

between belimumab and SoC. See Table 75. After removing decade of entry from the analysis

there still was not a statistically significant difference (p=0.715) in AMS over 5 years between

belimumab and SoC. See Table 76.

82

Table 75. Regression model AMS through year 5 with decade of baseline

Variable


[95% CI] P value


[95% CI] P value

Intercept 5.4179 (0.5943)

95% CI: [4.2451 ; 6.5907] p<0.001

5.4179 (0.5844) 95% CI: [4.2726 ; 6.5632]

p<0.001

Belimumab 0.0640 (0.4750)

95% CI: [-0.8734 ; 1.0013] p=0.893

0.0640 (0.4695) 95% CI: [-0.8563 ; 0.9842]

p=0.892

Entry Decade 2000 -1.0174 (0.7021)

95% CI: [-2.4028 ; 0.3680] p=0.149

-1.0174 (0.6516) 95% CI: [-2.2944 ; 0.2596]

p=0.118

Entry Decade 2010 -1.7400 (1.0132)

95% CI: [-3.7395 ; 0.2594] p=0.088

-1.7400 (0.9450) 95% CI: [-3.5921 ; 0.1120]

p=0.066

Abbreviations: AMS, adjusted mean SLEDAI; CI, confidence interval; OLS, ordinary least squares; SE, standard error; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index

Table 76. Regression model AMS through year 5 without decade of baseline

Variable


[95% CI] P value


[95% CI] P value

Intercept 4.5974 (0.3063)

95% CI: [3.9930 ; 5.2018] p<0.001

4.5974 (0.2636) 95% CI: [4.0806 ; 5.1141]

p<0.001

Belimumab -0.1647 (0.4332)

95% CI: [-1.0195 ; 0.6901] p=0.704

-0.1647 (0.4516) 95% CI: [-1.0498 ; 0.7205]

p=0.715

Abbreviations: AMS, adjusted mean SLEDAI; CI, confidence interval; OLS, ordinary least squares; SE, standard error; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index


interval

The 5-year cumulative average daily corticosteroid usage was analyzed using linear regression

with a binary indicator for treatment with belimumab as a covariate. All PSM variables were

balanced (Table 11) so none were added as covariates. The decade of entry was initially

included as a covariate (Table 77), but was not statistically significant (p=0.407 and p=0.376,

83

respectively). Controlling for decade of entry belimumab was associated with a significantly

lower (p=0.011) 5-year mean daily steroid dose. See Table 77. For subjects who entered their

respective studies in the same decade belimumab subjects had a lower mean daily dose of over

2 mg/kg. After removing decade of entry the conclusion was unchanged; belimumab subjects

had a statistically significant (p=0.002) 5-year mean daily corticosteroid usage 2.35 mg/kg lower

than patients receiving SoC. See Table 78.

Table 77. Regression model cumulative corticosteroid usage through year 5 controlled

for decade of entry

Variable


[95% CI] P value


[95% CI] P value

Intercept 8.3490 (1.1466)

95% CI: [6.0875 ; 10.6105] p<0.001

8.3490 (1.3309) 95% CI: [5.7404 ; 10.9575]

p<0.001

Belimumab -2.0445 (0.9031)

95% CI: [-3.8258 ; -0.2632] p=0.025

-2.0445 (0.8061) 95% CI: [-3.6245 ; -0.4646]

p=0.011

Entry Decade 2000 -1.2248 (1.3496)

95% CI: [-3.8866 ; 1.4372] p=0.365

-1.2248 (1.4781) 95% CI: [-4.1218 ; 1.6723]

p=0.407

Entry Decade 2010 -1.7979 (1.8878)

95% CI: [-5.5214 ; 1.9256] p=0.342

-1.7979 (2.0297) 95% CI: [-5.7761 ; 2.1803]

p=0.376

Abbreviations: CI, confidence interval; OLS, ordinary least squares; SE, standard error

Table 78. Regression model cumulative corticosteroid usage through year 5 without

decade of entry covariates

Variable


[95% CI] P value


[95% CI] P value

Intercept 7.4633 (0.5860)

95% CI: [6.3074 ; 8.6192] p<0.001

7.4633 (0.5888) 95% CI: [6.3092 ; 8.6174]

p<0.001

Belimumab -2.3504 (0.8287)

95% CI: [-3.9850 ; -0.7158] p=0.005

-2.3504 (0.7724) 95% CI: [-3.8643 ; -0.8365]

p=0.002

Abbreviations: CI, confidence interval; OLS, ordinary least squares; SE, standard error

84

6.2 Exploratory Analyses

6.2.1 Propensity score matching

6.2.1.1 Pooled LTE and TLC Patients with 5-years follow up


over the entire sample of 973 patients. The range of the PS distribution (Table 80) was -5.730 to


and TLC patient was -3.125 to 4.522, illustrated in Figure 3. With the caliper value of 0.402


4.924. Thirty TLC patients and zero LTE patients with PS values outside of the range of support

(including the caliper) cannot be matched.



85


dataset with 5 years follow-up (N=973)


Intercept 0.001 0.001 -6.76 <0.001

Age 1.265 0.051 5.80 <0.001

Age Squared 0.997 0.000 -5.24 <0.001

Female 1.325 0.402 0.93 0.354

Black 0.766 0.215 -0.95 0.342

Asian/Other Race 2.807 0.575 5.04 <0.001

SLE Duration 0.987 0.015 -0.84 0.402

Hypertension 2.047 0.444 3.30 0.001

Dyslipidemia 0.086 0.018 -11.50 <0.001

Proteinuria 0.450 0.101 -3.57 <0.001

ACR Criteria 1.173 0.082 2.30 0.022

Baseline SLEDAI 0.922 0.020 -3.78 <0.001

Corticosteroid Use 3.858 0.837 6.22 <0.001

Antimalarial Use 1.945 0.343 3.77 <0.001

Immunosuppressive Use 2.027 0.390 3.67 <0.001

Baseline SDI = 1 2.026 0.496 2.88 0.004

Baseline SDI = 2+ 3.730 1.173 4.19 <0.001

Abbreviations: ACR, American College of Rheumatology; LTE, long term extension; SDI, SLICC/ACR Damage Index; SE, standard error; SLE, systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; TLC, Toronto Lupus Cohort

Table 80. Summary statistics of PSM variable, pooled LTE and TLC dataset with 5 years

follow-up (N=973)

Statistic Value

Observations 973

Mean (SD) 0.619 (2.001)

Range -5.730, 4.866


Abbreviation: LTE, long term extension trial; SD, standard deviation; TLC, Toronto Lupus Cohort

86

Figure 3. Common support in full model with all patients (n=973)


larger than 10% for all of the variables (mean bias = 31.5%).


82). Bias is less than 5% for all variables except antimalarial (14.9%) and immunosuppressive

(7.8%). The mean bias is 3.7%.

-6-4

-20

24

Pre

dic

ted

PS

Va

lue

(X

B)

LTE_OUS LTE_US TLC

87

Table 81. Bias prior to PS matching, pooled LTE and TLC dataset with 5 years follow-up

(N=973)

Mean t-test


Age 39.674 37.336 19.3 3.00 0.003

Age Squared 1693.9 1565.8 12.8 1.99 0.047

Female 0.927 0.895 11.4 1.76 0.078

Black 0.091 0.150 -18.0 -2.80 0.005

Asian/Other Race 0.471 0.234 51.3 7.68 <0.001

SLE Duration 6.683 5.738 13.9 2.16 0.031

Hypertension 0.426 0.370 11.4 1.73 0.085

Dyslipidemia 0.132 0.570 -103.1 -16.36 <0.001

Proteinuria 0.167 0.312 -34.4 -5.37 <0.001

ACR Criteria 5.932 5.646 20.8 3.18 0.002

Baseline SLEDAI 8.027 10.016 -42.5 -6.64 <0.001

Corticosteroid use 0.843 0.606 54.9 8.63 <0.001



Baseline SDI = 1 0.233 0.150 21.3 3.19 0.001

Baseline SDI = 2+ 0.181 0.105 21.8 3.23 0.001

Abbreviations: ACR, American College of Rheumatology; LTE, long term extension; PS, propensity score; SDI, SLICC/ACR Damage Index; SE, standard error; SLE, systemic lupus erythematosus; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index; TLC, Toronto Lupus Cohort

88

Table 82. Bias post PS matching, pooled LTE and TLC dataset with 5 years follow-up

(n=362)

Mean t-test


Age 39.337 39.105 1.9 0.18 0.858

Age Squared 1691.8 1685.5 0.6 0.06 0.953

Female 0.895 0.906 -3.7 -0.35 0.726

Black 0.116 0.133 -5.0 -0.48 0.634

Asian/Other Race 0.315 0.331 -3.5 -0.34 0.737

SLE Duration 7.044 6.946 1.3 0.12 0.901

Hypertension 0.403 0.409 -1.1 -0.11 0.915

Dyslipidemia 0.326 0.309 3.6 0.34 0.736

Proteinuria 0.210 0.204 1.4 0.13 0.897

ACR Criteria 5.856 5.823 2.5 0.23 0.815

Baseline SLEDAI 9.094 8.912 4.4 0.42 0.675



Immunosuppressive Use 0.431 0.392 7.8 0.75 0.456

Baseline SDI = 1 0.204 0.193 2.8 0.26 0.793

Baseline SDI = 2+ 0.177 0.160 4.4 0.42 0.675


6.2.1.2 Pooled LTE and TLC Patients with ≥ 1-year follow up for time to event analyses


over the entire sample of 1,541 patients. The range of the PS distribution (Table 84) was -5.403

to 4.428. The range of common support (the range of “overlap” in the PS distributions) for the

LTE and TLC patient was -3.192 to 4.214, illustrated in Figure 4. With the caliper value of 0.343


4.558. The twenty TLC patients and the seven LTE patients with PS values outside of the range

of support (including the caliper) cannot be matched.



89


dataset with ≥ 1 year follow-up (N=1541)


Intercept 0.002 0.001 -9.02 <0.001

Age 1.250 0.037 7.62 <0.001

Age Squared 0.998 0.000 -6.87 <0.001

Female 2.074 0.488 3.10 0.002

Black 0.493 0.116 -3.02 0.003

Asian/Other Race 1.793 0.269 3.90 <0.001

SLE Duration 0.960 0.011 -3.70 <0.001

Hypertension 1.607 0.254 3.01 0.003

Dyslipidemia 0.129 0.021 -12.44 <0.001

Proteinuria 0.386 0.066 -5.60 <0.001

ACR Criteria 1.238 0.063 4.17 <0.001

Baseline SLEDAI 0.920 0.015 -5.27 <0.001

Corticosteroid Use 4.845 0.807 9.48 <0.001

Antimalarial Use 1.296 0.176 1.91 0.057

Immunosuppressive Use 1.577 0.224 3.22 0.001

Baseline SDI = 1 2.242 0.423 4.28 <0.001

Baseline SDI = 2+ 3.713 0.853 5.71 <0.001


Table 84. Summary statistics of PSM variable, pooled LTE and TLC dataset with ≥ 1 year

follow-up (N=1541)

Statistic Value

Observations 1541

Mean (SD) 0.639 (1.717)

Range -5.403, 4.428


Abbreviation: LTE, long term extension; SD, standard deviation; TLC, Toronto Lupus Cohort

90

Figure 4. Common support in full model with all patients (N=1541)


larger than 10% for most of the variables (mean bias = 26.7%).


86). Bias is less than less than 10% for all variables and less than 5% for all but five variables,

with the largest bias belonging to the baseline SDI greater than or equal to two variable (9.8%).

The mean bias is 3.7%.

-6-4

-20

24

Pre

dic

ted

PS

Va

lue

(X

B)

LTE_OUS LTE_US TLC

91

Table 85. Bias prior to PS matching, pooled LTE and TLC dataset with ≥ 1 year follow-up

(N=1541)

Mean t-test


Age 38.782 36.735 16.2 3.16 0.002

Age Squared 1634.0 1538.5 9.0 1.77 0.078

Female 0.942 0.885 20.4 4.04 <0.001

Black 0.075 0.150 -24.1 -4.77 <0.001

Asian/Other Race 0.457 0.301 32.7 6.19 <0.001

SLE Duration 6.737 6.358 5.5 1.07 0.283

Hypertension 0.400 0.383 3.3 0.63 0.528

Dyslipidemia 0.128 0.471 -80.7 -16.14 <0.001

Proteinuria 0.169 0.346 -41.3 -8.11 <0.001

ACR Criteria 5.971 5.674 21.6 4.12 <0.001

Baseline SLEDAI 8.273 10.100 -39.8 -7.76 <0.001

Corticosteroid use 0.859 0.639 52.5 10.40 <0.001



Baseline SDI = 1 0.241 0.150 22.9 4.29 <0.001

Baseline SDI = 2+ 0.177 0.103 21.3 3.97 <0.001


92

Table 86. Bias post PS matching, pooled LTE and TLC dataset with ≥ 1 year follow-up

(n=646)

Mean t-test


Age 38.108 37.416 5.4 0.69 0.492

Age Squared 1611.1 1566.9 4.2 0.53 0.598

Female 0.926 0.913 4.5 0.58 0.564

Black 0.115 0.108 2.0 0.25 0.803

Asian/Other Race 0.337 0.362 -5.2 -0.66 0.510

SLE Duration 7.061 6.803 3.6 0.46 0.648

Hypertension 0.412 0.402 1.9 0.24 0.810

Dyslipidemia 0.279 0.282 -0.7 -0.09 0.930

Proteinuria 0.245 0.263 -4.3 -0.54 0.588

ACR Criteria 5.901 5.892 0.7 0.09 0.931

Baseline SLEDAI 9.105 9.046 1.4 0.18 0.856



Immunosuppressive Use 0.399 0.409 -1.9 -0.24 0.810

Baseline SDI = 1 0.235 0.195 9.8 1.24 0.214

Baseline SDI = 2+ 0.133 0.139 -1.8 -0.23 0.819


6.2.2 Difference in change in SDI from baseline to 5 years

The total SDI score change from baseline to 5 years was evaluated using linear regression with

a binary indicator for treatment with belimumab as a covariate. 262 pooled LTE patients were

matched to 262 TLC patients using PSM. All but one of the PSM variables (antimalarial) were

balanced (Table 82). Therefore, an indicator variable for antimalarial use at baseline was

included as a covariate. The baseline decade of entry was also included as a covariate, with

1990 as the reference level.

When controlling for antimalarial use and decade of entry, the difference from baseline in the

fifth year total SDI score was significantly (p<0.001) lower for subjects taking belimumab. See

Table 87. None of the other covariates were statistically significant.

93

Without controlling for antimalarial use and decade of entry, the difference from baseline in the

fifth year total SDI score increased slightly but was still significantly (p<0.001) lower for subjects

taking belimumab. See Table 88.

Table 87. Year 5 Total SDI difference of change from baseline controlled for entry decade

and antimalarial use

Variable


[95% CI] P value


[95% CI] P value

Intercept 0.7183 (0.1489)

95% CI: [0.4254 ; 1.0112] p<0.001

0.7183 (0.1774) 95% CI: [0.3706 ; 1.0659]

p<0.001

Belimumab -0.4913 (0.1075)

95% CI: [-0.7026 ; -0.2800] p<0.001

-0.4913 (0.1210) 95% CI: [-0.7284 ; -0.2542]

p<0.001

Antimalarial use -0.0443 (0.1018)

95% CI: [-0.2446 ; 0.1560] p=0.664

-0.0443 (0.1011) 95% CI: [-0.2424 ; 0.1538]

p=0.661

Entry Decade 2000 0.0713 (0.1648)

95% CI: [-0.2528 ; 0.3954] p=0.665

0.0713 (0.1954) 95% CI: [-0.3117 ; 0.4543]

p=0.715

Entry Decade 2010 -0.2453 (0.2610)

95% CI: [-0.7586 ; 0.2680] p=0.348

-0.2453 (0.2266) 95% CI: [-0.6894 ; 0.1989]

p=0.279


94


Variable


[95% CI] P value


[95% CI] P value

Intercept 0.7182 (0.0687)

95% CI: [0.5830 ; 0.8534] p<0.001

0.7182 (0.0871) 95% CI: [0.5475 ; 0.8890]

p<0.001

Belimumab -0.4530 (0.0972)

95% CI: [-0.6442 ; -0.2619] p<0.001

-0.4530 (0.0984) 95% CI: [-0.6460 ; -0.2601]

p<0.001



The time to the first worsening (increase) in total SDI score was analyzed using parametric

survival models with a binary indicator for treatment with belimumab as the covariate. All PSM

variables had a bias less than 10%, however, the bias for the baseline SDI score of greater than

or equal to two variable was 9.8%. (Table 86). Therefore, baseline SDI score was included as a

covariate with the same levels used in the propensity score matching. The baseline decade of

entry also was included as a covariate.

Models with exponential, Weibull, Gompertz, log logistic, and log normal distributions were

evaluated (Table 89 to Table 94). Neither the baseline SDI score covariate nor the decade of

entry covariate was statistically significant for any of the distributions. In fact, there was only a

minor change in the coefficient estimate for belimumab when both covariates were excluded.

Since their impact was negligible, the results with all covariates are shown only for the

exponential distribution (Table 89).

Results indicated that belimumab was associated with a significantly lower rate of organ

damage progression, p<0.001, regardless of the distribution used.

95

Table 89. Proportional hazards model of time to first change in total SDI score controlling

for baseline SDI score and decade of study entry, exponential distribution

Variable


Estimate (SE)

[95% CI]

Hazard Rate/Ratio

Estimate (SE)

[95% CI] p value

Intercept -2.2259 (0.2294) 95% CI: [-2.6756 ; -1.7762]

0.1080 (0.0248) 95% CI: [0.0689 ; 0.1693] <0.001

Belimumab -0.8796 (0.2143) 95% CI: [-1.2996 ; -0.4596]

0.4150 (0.0889) 95% CI: [0.2726 ; 0.6315] <0.001

Baseline SDI = 1 -0.0783 (0.2120)

95% CI: [-0.4938 ; 0.3371] 0.9247 (0.1960)

95% CI: [0.6103 ; 1.4009] 0.712

Baseline SDI = 2+ -0.1051 (0.2635)

95% CI: [-0.6216 ; 0.4113] 0.9002 (0.2372)

95% CI: [0.5371 ; 1.5088] 0.690

Entry Decade 2000

-0.3348 (0.2679) 95% CI: [-0.8599 ; 0.1904]

0.7155 (0.1917) 95% CI: [0.4232 ; 1.2097] 0.211

Entry Decade 2010

-0.4514 (0.3304) 95% CI: [-1.0990 ; 0.1962]

0.6367 (0.2104) 95% CI: [0.3332 ; 1.2167] 0.172

Abbreviations: CI, confidence interval; SE, standard error; SDI, SLICC/ACR Damage Index

Table 90. Proportional hazards model of time to first change in total SDI score,


Variable


Estimate (SE)

[95% CI]

Hazard Rate/Ratio

Estimate (SE)

[95% CI] p value

Intercept -2.5477 (0.1045) 95% CI: [-2.7526 ; -2.3429]

0.0783 (0.0082) 95% CI: [0.0638 ; 0.0961]

<0.001

Belimumab -0.9244 (0.1866) 95% CI: [-1.2901 ; -0.5587]

0.3968 (0.0740) 95% CI: [0.2752 ; 0.5720]

<0.001



distribution.

Variable


Estimate (SE)

[95% CI]

Hazard Rate/Ratio

Estimate (SE)

[95% CI] p value

Intercept -2.3613 (0.1362) 95% CI: [-2.6282 ; -2.0944]

0.0943 (0.0128) 95% CI: [0.0722 ; 0.1231]

<0.001

Belimumab -0.9491 (0.1838) 95% CI: [-1.3095 ; -0.5888]

0.3871 (0.0712) 95% CI: [0.2700 ; 0.5550]

<0.001


96


distribution.

Variable


Estimate (SE)

[95% CI]

Hazard Rate/Ratio

Estimate (SE)

[95% CI] p value

Intercept -2.6613 (0.1284) 95% CI: [-2.9130 ; -2.4096]

0.0699 (0.0090) 95% CI: [0.0543 ; 0.0899] <0.001

Belimumab -0.9204 (0.1885) 95% CI: [-1.2898 ; -0.5510]

0.3984 (0.0751) 95% CI: [0.2753 ; 0.5764] <0.001

p 1.0673 (0.0483) 95% CI: [0.9767 ; 1.1664] <0.001

Abbreviations: CI, confidence interval; NA, not applicable; SE, standard error; SDI, SLICC/ACR Damage Index


loglogistic distribution

Variable


Estimate (SE)

[95% CI] p value

Intercept 2.1233 (0.1203) 95% CI: [1.8876 ; 2.3590]

<0.001

Belimumab 0.9164 (0.1769) 95% CI: [0.5697 ; 1.2632]

<0.001

gamma 0.8110 (0.0376) 95% CI: [0.7405 ; 0.8882]

<0.001



lognormal distribution

Variable


Estimate (SE)

[95% CI] p value

Intercept 2.1820 (0.1252) 95% CI: [1.9366 ; 2.4274]

<0.001

Belimumab 0.8845 (0.1599) 95% CI: [0.5712 ; 1.1978]

<0.001

sigma 1.4238 (0.0646) 95% CI: [1.3027 ; 1.5562]

<0.001


97

The information criterion scores for the models are displayed in Table 95. The lognormal

distribution produced substantially better measures of fit (lower AIC and BIC scores) than the

other distributions.

Table 95. Fit of regression models of time to first change in total SDI score

Distribution AIC BIC

Exponential 1100.0 1108.9

Gompertz 1098.7 1112.1

Loglogistic 1090.8 1104.2

Weibull 1101.1 1114.5

Lognormal 1074.9 1088.3

Abbreviations: AIC, Akaike information criterion; BIC, Bayesian information criterion; SDI, SLICC/ACR Damage Index


The counts and proportions of subjects in each treatment arm, out to year 5, of the incremental

changes from baseline of total SDI score are displayed in Table 96. There is a substantial

difference immediately. In the first year 32 (17.7%) SoC subjects had an increase in their total

SDI with 13 (7.2%) of those subjects experiencing an increase of greater than 1. In contrast,

only 14 (7.7%) belimumab subjects had an increase with 2 (1.1%) subjects having an increase

of 2. By the fifth year only 11 (6.1%) belimumab subjects had an increase greater than 1

whereas 30 (16.6%) SoC subjects had an increase greater than 1. Overall, by the fifth year 145

(80.1%) of belimumab subjects saw no change, while 107 (59.1%) SoC subjects saw no

change.

Table 96. SDI change from baseline

Year 1 Year 2 Year 3 Year 4 Year 5

SDI

Change

SoC

N=181

Belim

N=181

SoC

N=181

Belim

N=181

SoC

N=181

Belim

N=181

SoC

N=181

Belim

N=181

SoC

N=181

Belim

N=181

0 [n (%)] 149

(82.3%) 167

(92.3%) 135

(74.6%) 162

(89.5%) 123

(68.0%) 150

(82.9%) 113

(62.4%) 149

(82.3%) 107

(59.1%) 145

(80.1%)

+1 [n (%)] 19

(10.5%) 12

(6.6%) 28

(15.5%) 15

(8.3%) 36

(19.9%) 23

(12.7%) 39

(21.5%) 23

(12.7%) 44

(24.3%) 25

(13.8%)

+2 [n (%)] 9

(5.0%) 2

(1.1%) 13

(7.2%) 4

(2.2%) 14

(7.7%) 7

(3.9%) 19

(10.5%) 8

(4.4%) 18

(9.9%) 10

(5.5%)

+3 [n (%)] 1

(0.6%) 0

1 (0.6%)

0 2

(1.1%) 1

(0.6%) 3

(1.7%) 1

(0.6%) 4

(2.2%) 1

(0.6%)

+4 [n (%)] 3

(1.7%) 0

3 (1.7%)

0 4

(2.2%) 0

5 (2.8%)

0 4

(2.2%) 0

98

+5 [n (%)] 0 0 0 0 1

(0.6%) 0

1 (0.6%)

0 3

(1.7%) 0

+6 [n (%)] 0 0 1

(0.6%) 0

1 (0.6%)

0 0 0 0 0

+7[n (%)] 0 0 0 0 0 0 1

(0.6%) 0

1 (0.6%)

0










[95% CI] P value

Odds Ratio [95% CI]


-1.5151 (0.1884) 95% CI: [-1.8844 ; -1.1458]

p<0.001

NA

Belimumab

-1.0123 (0.3150) 95% CI: [-1.6296 ; -0.3949]

p=0.001

0.3634 95% CI: [0.1960 ; 0.6737]


-0.4596 (0.3266) 95% CI: [-1.0996 ; 0.1805]

p=0.159

NA


1 0.28 (0.595)

Abbreviations: ΔSDI, SDI change from baseline; CI, confidence interval; NA, not applicable; SDI, SLICC/ACR Damage Index; SoC, standard of care





99





[95% CI] P value

Odds Ratio [95% CI]


-1.1115 (0.1677) 95% CI: [-1.4401 ; -0.7829]

p<0.001

NA

Belimumab

-1.0065 (0.2697) 95% CI: [-1.5351 ; -0.4779]

p<0.001

0.3655 95% CI: [0.2154 ; 0.6201]


-0.4583 (0.2781) 95% CI: [-1.0033 ; 0.0868]

p=0.099

NA


1 0.10 (0.756)

Abbreviations: ΔSDI, SDI change from baseline; CI, confidence interval; NA, not applicable; SDI, SLICC/ACR Damage Index







100



[95% CI] P value

Odds Ratio [95% CI]


-0.7930 (0.1542) 95% CI: [-1.0952 ; -0.4907]

p<0.001

NA

Belimumab

-0.7482 (0.2259) 95% CI: [-1.1910 ; -0.3053]

p<0.001

0.4732 95% CI: [0.3039 ; 0.7369]


-0.4700 (0.2397) 95% CI: [-0.9397 ; -0.0002]

p=0.050

NA


1 0.27 (0.603)





change from baseline (Table 100). The odds of an increase from baseline total SDI score and

the odds of having an increase greater than one given an increase were each 2.5291 (1 /

0.3954) times greater for subjects receiving SoC versus subjects taking belimumab.

101



[95% CI] P value

Odds Ratio [95% CI]


-0.5569 (0.1483) 95% CI: [-0.8476 ; -0.2662]

p<0.001

NA

Belimumab

-0.9279 (0.2189) 95% CI: [-1.3569 ; -0.4989]

p<0.001

0.3954 95% CI: [0.2575 ; 0.6072]


-0.2441 (0.2205) 95% CI: [-0.6762 ; 0.1881]

p=0.268

NA


1 0.59 (0.442)





change from baseline (Table 100). The odds of an increase from baseline total SDI score and

the odds of having an increase greater than one given an increase were each 2.3981 (1 /

0.4170) times greater for subjects receiving SoC versus subjects taking belimumab.

102



[95% CI] P value

Odds Ratio [95% CI]


-0.4377 (0.1456) 95% CI: [-0.7230 ; -0.1523]

p=0.003

NA

Belimumab

-0.8747 (0.2098) 95% CI: [-1.2859 ; -0.4635]

p<0.001

0.4170 95% CI: [0.2764 ; 0.6291]


-0.2820 (0.2117) 95% CI: [-0.6969 ; 0.1328]

p=0.183

NA


1 1.47 (0.226)



The change of total SDI score from baseline to end of years 1 through 5 was analyzed using

linear regression with a binary indicator for treatment with belimumab as a covariate. All but one

of the PSM variables (antimalarial) were balanced (Table 82). Therefore, an indicator variable

for antimalarial use at baseline was included as a covariate. The baseline decade of entry was

also included as a covariate with the reference level being 1990.

Neither the baseline decade of entry nor antimalarial use at baseline coefficients were

statistically significant for any of the five years from baseline and their simultaneous or individual

inclusion only had a slight impact on the magnitude of the belimumab coefficient. Therefore, the

regression results with baseline decade of entry and antimalarial use as covariates are omitted

for all but the first year.

With both baseline decade of entry and antimalarial use at baseline included as covariates, the

difference from baseline in the first year total SDI score was significantly (p=0.001) lower for

subjects taking belimumab. See Table 102. The average SDI change from baseline was lower

by 0.2311 for subjects taking belimumab compared to those receiving SoC when controlling for

decade of entry and antimalarial use at baseline.

103

Table 102. Year 1 Total SDI difference of change from baseline controlled for entry

decade and antimalarial use at baseline

Variable


[95% CI] P value


[95% CI] P value

Intercept 0.3000 (0.0909)

95% CI: [0.1213 ; 0.4786] p=0.001

0.2999 (0.1227) 95% CI: [0.0595 ; 0.5404]

p=0.014

Belimumab -0.2311 (0.0656)

95% CI: [-0.3600 ; -0.1022] p<0.001

-0.2311 (0.0727) 95% CI: [-0.3735 ; -0.0887]

p=0.001


95% CI: [-0.2171 ; 0.0273] p=0.128

-0.0949 (0.0665) 95% CI: [-0.2251 ; 0.0354]

p=0.153

Entry Decade 2000 0.0856 (0.1005)

95% CI: [-0.1121 ; 0.2833] p=0.395

0.0856 (0.1282) 95% CI: [-0.1657 ; 0.3369]

p=0.504

Entry Decade 2010 -0.1555 (0.1592)

95% CI: [-0.4685 ; 0.1576] p=0.329

-0.1555 (0.1214) 95% CI: [-0.3934 ; 0.0825]

p=0.200

When baseline decade of entry was included as a covariate the difference from baseline in the

first year total SDI score was significantly (p=0.001) lower for subjects taking belimumab. See

Table 103. The average SDI change from baseline was lower by 0.2343 for subjects taking

belimumab compared to those receiving SoC when controlling for decade of entry.

104

Table 103. Year 1 Total SDI difference of change from baseline controlled for entry

decade

Variable


[95% CI] P value


[95% CI] P value

Intercept 0.2558 (0.0863)

95% CI: [0.0861 ; 0.4255] p=0.003

0.2558 (0.1103) 95% CI: [0.0396 ; 0.4720]

p=0.020

Belimumab -0.2343 (0.0656)

95% CI: [-0.3633 ; -0.1052] p<0.001

-0.2343 (0.0736) 95% CI: [-0.3786 ; -0.0900]

p=0.001

Entry Decade 2000 0.0696 (0.1002)

95% CI: [-0.1274 ; 0.2665] p=0.488

0.0696 (0.1301) 95% CI: [-0.1854 ; 0.3245]

p=0.593

Entry Decade 2010 -0.1742 (0.1590)

95% CI: [-0.4870 ; 0.1385] p=0.274

-0.1742 (0.1238) 95% CI: [-0.4169 ; 0.0685]

p=0.159


When antimalarial use at baseline was included as a covariate the difference from baseline in

the first year total SDI score was significantly (p=0.001) lower for subjects taking belimumab.

See Table 104. The average SDI change from baseline was lower by 0.1923 for subjects taking

belimumab compared to those receiving SoC when controlling for antimalarial use at baseline.

105

Table 104. Year 1 Total SDI difference of change from baseline controlled for antimalarial

use at baseline

Variable


[95% CI] P value


[95% CI] P value

Intercept 0.3424 (0.0560)

95% CI: [0.2323 ; 0.4524] p<0.001

0.3424 (0.0772) 95% CI: [0.1911 ; 0.4936]

p<0.001

Belimumab -0.1923 (0.0596)

95% CI: [-0.3095 ; -0.0750] p=0.001

-0.1923 (0.0594) 95% CI: [-0.3086 ; -0.0759]

p=0.001


95% CI: [-0.2140 ; 0.0293] p=0.136

-0.0923 (0.0676) 95% CI: [-0.2249 ; 0.0403]

p=0.172


Without controlling for baseline decade of entry or antimalarial use the results were similar. The

change in total SDI score from baseline at the end of the first year was significantly (p=0.001)

lower for subjects taking belimumab. See Table 105. The average SDI change from baseline

was lower by 0.1989 for subjects taking belimumab compared to those receiving SoC.


Variable


[95% CI] P value


[95% CI] P value

Intercept 0.2873 (0.0421)

95% CI: [0.2044 ; 0.3701] p<0.001

0.2873 (0.0547) 95% CI: [0.1802 ; 0.3944]

p<0.001

Belimumab -0.1989 (0.0596)

95% CI: [-0.3161 ; -0.0817] p<0.001

-0.1989 (0.0610) 95% CI: [-0.3184 ; -0.0794]

p=0.001


For years 2 through 5 the change in total SDI score from baseline was always significantly lower

for belimumab, with the magnitude of the difference increasing year-to-year. See Table 106 to

Table 109. After two years the average SDI change from baseline was lower by 0.2873 for

subjects taking belimumab compared to those receiving SoC. The magnitude of the difference

106

at the end of year 3 was 0.3149. By the end of year 4 belimumab subjects had an average

difference that was lower by 0.4199. In the last year the belimumab average change from

baseline was 0.4530 less.


Variable


[95% CI] P value


[95% CI] P value

Intercept 0.4144 (0.0511)

95% CI: [0.3139 ; 0.5148] p<0.001

0.4144 (0.0661) 95% CI: [0.2848 ; 0.5439]

p<0.001

Belimumab -0.2873 (0.0722)

95% CI: [-0.4294 ; -0.1452] p<0.001

-0.2873 (0.0742) 95% CI: [-0.4328 ; -0.1418]

p<0.001

Abbreviations: CI, confidence interval; OLS, ordinary least squares; SDI, SLICC/ACR Damage Index; SE, standard

error


Variable


[95% CI] P value


[95% CI] P value

Intercept 0.5359 (0.0600)

95% CI: [0.4180 ; 0.6538] p<0.001

0.5359 (0.0751) 95% CI: [0.3888 ; 0.6830]

p<0.001

Belimumab -0.3149 (0.0848)

95% CI: [-0.4816 ; -0.1482] p<0.001

-0.3149 (0.0858) 95% CI: [-0.4830 ; -0.1468]

p<0.001


107


Variable


[95% CI] P value


[95% CI] P value

Intercept 0.6519 (0.0648)

95% CI: [0.5246 ; 0.7793] p<0.001

0.6519 (0.0821) 95% CI: [0.4910 ; 0.8129]

p<0.001

Belimumab -0.4199 (0.0916)

95% CI: [-0.6000 ; -0.2398] p<0.001

-0.4199 (0.0922) 95% CI: [-0.6006 ; -0.2392]

p<0.001



Variable


[95% CI] P value


[95% CI] P value

Intercept 0.7182 (0.0687)

95% CI: [0.5830 ; 0.8534] p<0.001

0.7182 (0.0871) 95% CI: [0.5475 ; 0.8890]

p<0.001

Belimumab -0.4530 (0.0972)

95% CI: [-0.6442 ; -0.2619] p<0.001

-0.4530 (0.0984) 95% CI: [-0.6460 ; -0.2601]

p<0.001



The annual transition probability of a change in SDI was estimated by combining the results

from the time to first SDI worsening analysis (Section 6.2.3) with the observed conditional

probability that the increase in SDI score was 1 point versus 2+ points. The resulting annual

probabilities are shown in Table 110. A constant hazard was assumed and time to first SDI

worsening was modeled using an exponential distribution (Table 90). The conditional

probabilities were derived separately using the observed counts for the specific treatment arm.

108

Table 110. Annual transition probabilities

SoC Belimumab

No SDI change 0.9247 0.9694



Abbreviations: SoC, standard of care


The counts and proportions of the incremental changes from baseline out to year 5 for each of

the SDI organ system subscores are displayed in the even numbered tables of Section 6.2.7.

Results of two-sided Fisher’s exact tests for each SDI organ system subscore are displayed in

the odd numbered tables.

The majority of the subscores showed no significant difference in the proportion of subjects with

a change from baseline at the end of year 5 or any prior year; the exceptions were

musculoskeletal and skin subscores. For the cardiovascular subscore a marginally significant

difference was recorded in the first year only. For the neuropsychiatric subscore a marginally

significant difference was seen in the fourth and fifth years.


ocular system subscore. See Table 111. For belimumab subjects, 14 (7.7%) had seen an

increase. The difference in the number of SoC subjects compared to belimumab was relatively

consistent across all five years.

Table 111. SDI ocular system subscore change from baseline by year

SDI

Ocular Year 1 Year 2 Year 3 Year 4 Year 5

Change

from Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 176

(97.2%) 175

(96.7%) 174

(96.1%) 174

(96.1%) 171

(94.5%) 170

(93.9%) 169

(93.4%) 170

(93.9%) 166

(91.7%) 167

(92.3%)

+1 [n (%)] 5

(2.8%) 6

(3.3%) 6

(3.3%) 7

(3.9%) 9

(5.0%) 11

(6.1%) 11

(6.1%) 11

(6.1%) 14

(7.7%) 14

(7.7%)

+2 [n (%)] 0 0 1

(0.6%) 0

1 (0.6%)

0 1

(0.6%) 0

1 (0.6%)

0


109

For the ocular system subscore there was no significant difference in the proportion of subjects



Table 112. Fisher’s test for belimumab versus SoC SDI ocular subscore change from

baseline


1 1.206 0.301 ; 5.094 1.000

2 1.000 0.293 ; 3.418 1.000

3 1.106 0.414 ; 2.989 1.000

4 0.912 0.354 ; 2.326 1.000

5 0.928 0.401 ; 2.135 1.000

Abbreviations: CI, confidence interval; SDI, SLICC/ACR Damage Index


neuropsychiatric system subscore. See Table 113. In comparison, only 4 (2.3%) belimumab

subjects had seen an increase. The difference in the number of SoC subjects compared to

belimumab subjects experiencing any increase was 3 in the first year and grew to 8 by the end

of year 5.

Table 113. SDI neuropsychiatric system subscore change from baseline by year

SDI

Neuro Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 177

(97.8%) 180

(99.4%) 176

(97.2%) 179

(98.9%) 172

(95.0%) 178

(98.3%) 170

(93.9%) 178

(98.3%) 169

(93.4%) 177

(97.8%)

+1 [n (%)] 3

(1.7%) 1

(0.6%) 4

(2.2%) 2

(1.1%) 7

(3.9%) 2

(1.1%) 9

(5.0%) 2

(1.1%) 10

(5.5%) 3

(1.7%)

+2 [n (%)] 0 0 0 0 1

(0.6%) 1

(0.6%) 1

(0.6%) 1

(0.6%) 1

(0.6%) 1

(0.6%)

+3 [n (%)] 1

(0.6%) 0

1 (0.6%)

0 1

(0.6%) 0

1 (0.6%)

0 1

(0.6%) 0


For the neuropsychiatric system subscore, by the end of the 5th year the odds ratio was

marginally significant (p=0.070), with the odds of experiencing an increase from their baseline

score being lower for belimumab subjects compared to SoC subjects. The results were similar

110

in the fourth year (p=0.053). There was no significant difference seen in the first three years.

See Table 114.

Table 114. Fisher’s test for belimumab versus SoC SDI neuropsychiatric system



1 0.247 0.005 ; 2.524 0.372

2 0.394 0.037 ; 2.447 0.449

3 0.323 0.055 ; 1.323 0.139

4 0.261 0.046 ; 1.012 0.053

5 0.319 0.074 ; 1.080 0.070


At the end of the fifth year 4 (2.2%) SoC subjects had seen an increase from their baseline renal

system subscore (Table 115) while 1 (0.6%) belimumab subject saw an increase by the end of

the fifth year. The difference in the number of SoC subjects compared to belimumab subjects

experiencing any increase was the same over all five years.

Table 115. SDI renal system subscore change from baseline by year

SDI

Renal Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 178

(98.3%) 181

(100.0%) 178

(98.3%) 181

(100.0%) 177

(97.8%) 180

(99.4%) 177

(97.8%) 180

(99.4%) 177

(97.8%) 180

(99.4%)

+1 [n (%)] 3

(1.7%) 0

3 (1.7%)

0 4

(2.2%) 1

(0.6%) 4

(2.2%) 1

(0.6%) 4

(2.2%) 1

(0.6%)


For the renal system subscore there was no significant difference in the proportion of subjects



111

Table 116. Fisher’s test for belimumab versus SoC SDI renal system subscore change

from baseline


1 0 0 ; 2.412 0.248

2 0 0 ; 2.412 0.248

3 0.247 0.005 ; 2.524 0.372

4 0.247 0.005 ; 2.524 0.372

5 0.247 0.005 ; 2.524 0.372



pulmonary system subscore, whereas no belimumab subjects saw an increase. See Table 117.

Table 117. SDI pulmonary system subscore change from baseline by year

SDI

Pulmonary Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 180

(99.4%) 181

(100.0%) 178

(98.3%) 181

(100.0%) 178

(98.3%) 181

(100.0%) 177

(97.8%) 181

(100.0%) 177

(97.8%) 181

(100.0%)

+1 [n (%)] 1

(0.6%) 0

3 (1.7%)

0 3

(1.7%) 0

3 (1.7%)

0 3

(1.7%) 0

+2 [n (%)] 0 0 0 0 0 0 1

(0.6%) 0

1 (0.6%)

0


For the pulmonary system subscore there was no significant difference in the proportion of



112

Table 118. Fisher’s test for belimumab versus SoC SDI pulmonary system subscore



1 0 0 ; 39.001 1.000

2 0 0 ; 2.412 0.248

3 0 0 ; 2.412 0.248

4 0 0 ; 1.505 0.123

5 0 0 ; 1.505 0.123



cardiovascular system subscore. See Table 119. For belimumab subjects, 5 (2.8%) had seen

an increase. Nearly half of the SoC subjects who experienced an increase did so in the first

year.

Table 119. SDI cardiovascular system subscore change from baseline by year

SDI

CV Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 176

(97.2%) 181

(100.0%) 175

(96.7%) 180

(99.4%) 173

(95.6%) 177

(97.8%) 172

(95.0%) 177

(97.8%) 170

(93.9%) 176

(97.2%)

+1 [n (%)] 5

(2.8%) 0

6 (3.3%)

0 7

(3.9%) 3

(1.7%) 8

(4.4%) 3

(1.7%) 10

(5.5%) 4

(2.2%)

+2 [n (%)] 0 0 0 1

(0.6%) 0

1 (0.6%)

0 1

(0.6%) 0

1 (0.6%)

+3 [n (%)] 0 0 0 0 1

(0.6%) 0

1 (0.6%)

0 0 0

+4 [n (%)] 0 0 0 0 0 0 0 0 1

(0.6%) 0

Abbreviations: Belim, belimumab; CV, cardiovascular; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the cardiovascular system subscore there was no significant difference in the proportion of


the end of years 2 through 4. See Table 120. The odds ratio in the first year was marginally

significant (p=0.061), with the odds being lower for belimumab subjects compared to those

receiving SoC.

113

Table 120. Fisher’s test for belimumab versus SoC SDI cardiovascular subscore change

from baseline


1 0 0 ; 1.080 0.061

2 0.163 0.004 ; 1.362 0.121

3 0.490 0.106 ; 1.868 0.380

4 0.433 0.096 ; 1.586 0.258

5 0.440 0.117 ; 1.409 0.200

Abbreviations: CI, confidence interval; CV, cardiovascular; SDI, SLICC/ACR Damage Index; SoC, standard of care


peripheral vascular system subscore. See Table 121. For belimumab subjects, 4 (2.2%) had

seen an increase. The difference in the number of SoC subjects compared to belimumab

subjects experiencing any increase grew or remained the same from year to year.

Table 121. SDI peripheral vascular system subscore change from baseline by year

SDI

PV Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 179

(98.9%) 179

(98.9%) 178

(98.3%) 178

(98.3%) 177

(97.8%) 177

(97.8%) 176

(97.2%) 177

(97.8%) 175

(96.7%) 177

(97.8%)

+1 [n (%)] 2

(1.1%) 2

(1.1%) 3

(1.7%) 3

(1.7%) 4

(2.2%) 3

(1.7%) 5

(2.8%) 3

(1.7%) 6

(3.3%) 3

(1.7%)

+2 [n (%)] 0 0 0 0 0 1

(0.6%) 0

1 (0.6%)

0 1

(0.6%)

Abbreviations: Belim, belimumab; PV, peripheral vascular; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the peripheral vascular system subscore there was no significant difference in the

proportion of subjects with an increase from their baseline score by the end of the 5th year

(p=0.750) or at the end of any of the prior years. See Table 122.

114

Table 122. Fisher’s test for belimumab versus SoC SDI peripheral vascular system



1 1.000 0.072 ; 13.931 1.000

2 1.000 0.132 ; 7.568 1.000

3 1.000 0.183 ; 5.458 1.000

4 0.796 0.155 ; 3.765 1.000

5 0.660 0.135 ; 2.837 0.750



gastrointestinal system subscore. See Table 123. For belimumab subjects, 2 (1.1%) had seen

an increase. No changes occurred after the first year for belimumab subjects.

Table 123. SDI gastrointestinal system subscore change from baseline by year

SDI

GI Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 180

(99.4%) 179

(98.9%) 179

(98.9%) 179

(98.9%) 179

(98.9%) 179

(98.9%) 179

(98.9%) 179

(98.9%) 179

(98.9%) 179

(98.9%)

+1 [n (%)] 1

(0.6%) 2

(1.1%) 2

(1.1%) 2

(1.1%) 2

(1.1%) 2

(1.1%) 2

(1.1%) 2

(1.1%) 2

(1.1%) 2

(1.1%)

Abbreviations: Belim, belimumab; GI, gastrointestinal; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the gastrointestinal system subscore there was no significant difference in the proportion of






1 2.007 0.104 ; 119.220 1.000

2 1.000 0.072 ; 13.931 1.000

3 1.000 0.072 ; 13.931 1.000

4 1.000 0.072 ; 13.931 1.000

5 1.000 0.072 ; 13.931 1.000


115


musculoskeletal system subscore, with 13 (7.2%) of those being an increase greater than one.

See Table 125. In comparison, only 9 (5.0%) belimumab subjects had seen an increase with 2

(1.1%) being greater than one. The first year saw the largest number (17) of SoC subjects

experiencing an increase. Up until the 5th year, the difference in the number of SoC subjects

compared to belimumab subjects experiencing any increase grew year-to-year.

Table 125. SDI musculoskeletal system subscore change from baseline by year

SDI

MS Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 164

(90.6%) 177

(97.8%) 156

(86.2%) 176

(97.2%) 150

(82.9%) 174

(96.1%) 145

(80.1%) 173

(95.6%) 145

(80.1%) 172

(95.0%)

+1 [n (%)] 13

(7.2%) 3

(1.7%) 18

(9.9%) 3

(1.7%) 24

(13.3%) 5

(2.8%) 23

(12.7%) 6

(3.3%) 23

(12.7%) 7

(3.9%)

+2 [n (%)] 4

(2.2%) 1

(0.6%) 7

(3.9%) 2

(1.1%) 7

(3.9%) 2

(1.1%) 12

(6.6%) 2

(1.1%) 12

(6.6%) 2

(1.1%)

+3 [n (%)] 0 0 0 0 0 0 1

(0.6%) 0

1 (0.6%)

0

Abbreviations: Belim, belimumab; MS, musculoskeletal; SDI, SLICC/ACR Damage Index; SoC, standard of care

For the musculoskeletal system subscore the odds of belimumab subjects experiencing an

increase from their baseline score by the end of the 5th year were significantly lower compared

to SoC subjects (p<0.001). In fact, for belimumab subjects the odds were significantly less at

the end of the first year (p=0.006) and continued to be significantly less for the intervening

years. See Table 126.




1 0.219 0.053 ; 0.690 0.006

2 0.178 0.052 ; 0.489 <0.001

3 0.195 0.071 ; 0.469 <0.001

4 0.187 0.073 ; 0.426 <0.001

5 0.212 0.087 ; 0.466 <0.001


116


skin system subscore, with no increase for any belimumab subject. See Table 127. The first

year saw the largest number (5) of SoC subjects experiencing an increase. SoC subjects saw

an increase each year except in the fourth year.

Table 127. SDI skin system subscore change from baseline by year

SDI

Skin Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 176

(97.2%) 181

(100.0%) 175

(96.7%) 181

(100.0%) 173

(95.6%) 181

(100.0%) 173

(95.6%) 181

(100.0%) 170

(93.9%) 181

(100.0%)

+1 [n (%)] 5

(2.8%) 0

6 (3.3%)

0 8

(4.4%) 0

8 (4.4%)

0 10

(5.5%) 0

+2 [n (%)] 0 0 0 0 0 0 0 0 1

(0.6%) 0


For the skin system subscore the odds of belimumab subjects experiencing an increase from

their baseline score by the end of the 5th year were significantly lower compared to SoC

subjects (p<0.001). For belimumab subjects, the odds were significantly less for years 2 through

5 and marginally significant in the first year. See Table 128.

Table 128. Fisher’s test for belimumab versus SoC SDI skin change from baseline


1 0 0 ; 1.080 0.061

2 0 0 ; 0.836 0.030

3 0 0 ; 0.571 0.007

4 0 0 ; 0.571 0.007

5 0 0 ; 0.383 <0.001



premature gonadal failure subscore. See Table 129. One belimumab subject had an increase

by the end of the fifth year.

117

Table 129. SDI premature gonadal failure subscore change from baseline by year

SDI

Gonadal Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 179

(98.9%) 181

(100.0%) 179

(98.9%) 181

(100.0%) 179

(98.9%) 181

(100.0%) 179

(98.9%) 180

(99.4%) 179

(98.9%) 180

(99.4%)

+1 [n (%)] 2

(1.1%) 0

2 (1.1%)

0 2

(1.1%) 0

2 (1.1%)

1 (0.6%)

2 (1.1%)

1 (0.6%)


For the premature gonadal failure subscore there was no significant difference in the proportion

of subjects with an increase from their baseline score by the end of the 5th year (p=1.000) or at


Table 130. Fisher’s test for belimumab versus SoC SDI premature gonadal failure



1 0 0 ; 5.320 0.499

2 0 0 ; 5.320 0.499

3 0 0 ; 5.320 0.499

4 0.498 0.008 ; 9.648 1.000

5 0.498 0.008 ; 9.648 1.000



diabetes subscore. See Table 131. For belimumab subjects, 2 (1.1%) had seen an increase.

Table 131. SDI diabetes subscore change from baseline by year

SDI

Diabetes Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 180

(99.4%) 181

(100.0%) 179

(98.9%) 181

(100.0%) 179

(98.9%) 179

(98.9%) 178

(98.3%) 179

(98.9%) 178

(98.3%) 179

(98.9%)

+1 [n (%)] 1

(0.6%) 0

2 (1.1%)

0 2

(1.1%) 2

(1.1%) 3

(1.7%) 2

(1.1%) 3

(1.7%) 2

(1.1%)


118

For the diabetes subscore there was no significant difference in the proportion of subjects with

an increase from their baseline score by the end of the 5th year (p=1.000) or at the end of any

of the prior years. See Table 132.


baseline


1 0 0 ; 39.001 1.000

2 0 0 ; 5.320 0.499

3 1.000 0.072 ; 13.931 1.000

4 0.664 0.055 ; 5.866 1.000

5 0.664 0.055 ; 5.866 1.000


At the end of the fourth year 1 (0.6%) SoC subjects had seen an increase from their baseline

malignancy subscore, Table 133. Also, only 1 (0.6%) belimumab subject saw an increase.

Table 133. SDI malignancy subscore change from baseline by year

SDI

Malig Year 1 Year 2 Year 3 Year 4 Year 5

Change

From

Baseline

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

SoC N=181

Belim N=181

0 [n (%)] 181

(100.0%) 181

(100.0%) 180

(99.4%) 181

(100.0%) 180

(99.4%) 180

(99.4%) 180

(99.4%) 180

(99.4%) 180

(99.4%) 180

(99.4%)

1 [n (%)] 0 0 1

(0.6%) 0

1 (0.6%)

1 (0.6%)

1 (0.6%)

1 (0.6%)

1 (0.6%)

1 (0.6%)


For the malignancy subscore there was no significant difference in the proportion of subjects



119

Table 134. Fisher’s test for belimumab versus SoC SDI malignancy subscore change

from baseline


1 0 0 ; Inf 1.000

2 0 0 ; 39.001 1.000

3 1.000 0.013 ; 78.881 1.000

4 1.000 0.013 ; 78.881 1.000

5 1.000 0.013 ; 78.881 1.000

Abbreviations: Belim, belimumab; Inf, infinity; SDI, SLICC/ACR Damage Index; SoC, standard of care


subscores

The total change from baseline for SDI subscores was analyzed using linear regression with the

difference between the subject’s score in their final year and their baseline score used as the

response variable. An indicator variable for treatment with belimumab along with a categorical

variable for the decade of entry were included as covariates. The year from baseline was also

included to control for the length of time from baseline. All PSM variables had a bias less than

10%, however, the bias for the baseline SDI score of greater than or equal to two variable was

9.8% (Table 86). Therefore, baseline SDI score was included as a covariate with the same

levels used in the propensity score matching.

The data set for this analysis consisted of the 646 PS matched patients where subjects were not

restricted to at least 5-years of follow-up. A second analysis was performed using the 5th year of

the primary 362 PS matched patients. The results from this analysis were used to check the

robustness of the results where subjects’ scores were recorded in different years.

If either baseline decade or baseline SDI score or both were not (marginally) significant

predictors in the SDI subscale change from baseline and their inclusion had only a minor effect

on the belimumab coefficient then the results were omitted for models with these covariates.

The majority of the subscores showed no significant difference by treatment arm in the change

between the subject’s score in their final year and their baseline score; the lone exception was

the musculoskeletal subscore.

For the cardiovascular SDI system subscore there was no significant difference (p=0.382)

between belimumab and SoC in the change from baseline score when controlling for baseline

SDI score. See Table 135.

120

Table 135. SDI cardiovascular system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept -0.0075 (0.0189)

95% CI: [-0.0446 ; 0.0295] p=0.689

-0.0075 (0.0164) 95% CI: [-0.0397 ; 0.0246]

p=0.646

0.0700 (0.0253) 95% CI: [0.0204 ; 0.1197]

p=0.006

Belimumab -0.0144 (0.0164)

95% CI: [-0.0467 ; 0.0179] p=0.382

-0.0144 (0.0161) 95% CI: [-0.0460 ; 0.0172]

p=0.372

-0.0450 (0.0317) 95% CI: [-0.1074 ; 0.0174]

p=0.157

Baseline

SDI = 1

0.0350 (0.0204) 95% CI: [-0.0051 ; 0.0751]

p=0.087

0.0350 (0.0272) 95% CI: [-0.0183 ; 0.0883]

p=0.198

-0.0053 (0.0408) 95% CI: [-0.0854 ; 0.0749]

p=0.898

Baseline

SDI = 2+

0.0228 (0.0244) 95% CI: [-0.0251 ; 0.0708]

p=0.350

0.0228 (0.0234) 95% CI: [-0.0231 ; 0.0687]

p=0.329

0.0519 (0.0435) 95% CI: [-0.0336 ; 0.1374]

p=0.233

Final Year 0.0073 (0.0027)

95% CI: [0.0020 ; 0.0127] p=0.007

0.0073 (0.0026) 95% CI: [0.0023 ; 0.0124]

p=0.005

NA


When controlling for decade of entry, for the SDI diabetes subscore there was no significant

difference (p=0.811) between belimumab and SoC in the change from baseline score. See

Table 136.

121

Table 136. SDI diabetes change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0339 (0.0170)

95% CI: [0.0004 ; 0.0673] p=0.047

0.0339 (0.0230) 95% CI: [-0.0113 ; 0.0790]

p=0.141

0.0000 (0.0179) 95% CI: [-0.0351 ; 0.0351]

p=1.000

Belimumab -0.0034 (0.0127)

95% CI: [-0.0283 ; 0.0216] p=0.791

-0.0034 (0.0141) 95% CI: [-0.0309 ; 0.0242]

p=0.811

-0.0131 (0.0136) 95% CI: [-0.0398 ; 0.0136]

p=0.335

Entry Decade

2000

-0.0287 (0.0185) 95% CI: [-0.0650 ; 0.0076]

p=0.122

-0.0287 (0.0293) 95% CI: [-0.0861 ; 0.0288]

p=0.329

0.0244 (0.0207) 95% CI: [-0.0163 ; 0.0651]

p=0.240

Entry Decade

2010

-0.0405 (0.0192) 95% CI: [-0.0781 ; -0.0028]

p=0.035

-0.0405 (0.0239) 95% CI: [-0.0873 ; 0.0063]

p=0.090

0.0015 (0.0329) 95% CI: [-0.0632 ; 0.0661]

p=0.965

Final Year 0.0022 (0.0018)

95% CI: [-0.0012 ; 0.0056] p=0.211

0.0022 (0.0020) 95% CI: [-0.0017 ; 0.0061]

p=0.270

NA


For the SDI gastrointestinal subscore there was no significant difference (p=0.282) between

belimumab and SoC in the change from baseline score when controlling for decade of entry.

See Table 137.

122

Table 137. SDI gastrointestinal system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0331 (0.0179)

95% CI: [-0.0020 ; 0.0682] p=0.064

0.0331 (0.0346) 95% CI: [-0.0347 ; 0.1009]

p=0.338

0.0000 (0.0160) 95% CI: [-0.0315 ; 0.0315]

p=1.000

Belimumab -0.0159 (0.0133)

95% CI: [-0.0421 ; 0.0103] p=0.235

-0.0159 (0.0148) 95% CI: [-0.0448 ; 0.0131]

p=0.282

-0.0051 (0.0122) 95% CI: [-0.0290 ; 0.0189]

p=0.676

Entry Decade

2000

-0.0223 (0.0194) 95% CI: [-0.0604 ; 0.0158]

p=0.250

-0.0223 (0.0352) 95% CI: [-0.0914 ; 0.0467]

p=0.526

0.0163 (0.0186) 95% CI: [-0.0202 ; 0.0529]

p=0.380

Entry Decade

2010

-0.0400 (0.0201) 95% CI: [-0.0795 ; -0.0005]

p=0.047

-0.0400 (0.0329) 95% CI: [-0.1044 ; 0.0244]

p=0.224

0.0006 (0.0295) 95% CI: [-0.0574 ; 0.0586]

p=0.985

Final Year 0.0024 (0.0018)

95% CI: [-0.0013 ; 0.0060] p=0.201

0.0024 (0.0023) 95% CI: [-0.0022 ; 0.0069]

p=0.314

NA


For the SDI premature gonadal failure subscore there was no significant difference (p=0.543)


Table 138. SDI premature gonadal failure subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0003 (0.0059)

95% CI: [-0.0113 ; 0.0120] p=0.959

0.0003 (0.0026) 95% CI: [-0.0047 ; 0.0053]

p=0.905

0.0111 (0.0068) 95% CI: [-0.0022 ; 0.0243]

p=0.103

Belimumab 0.0033 (0.0054)

95% CI: [-0.0073 ; 0.0138] p=0.543

0.0033 (0.0053) 95% CI: [-0.0071 ; 0.0137]

p=0.537

-0.0055 (0.0096) 95% CI: [-0.0243 ; 0.0133]

p=0.563

Final Year 0.0005 (0.0009)

95% CI: [-0.0012 ; 0.0023] p=0.541

0.0005 (0.0007) 95% CI: [-0.0008 ; 0.0019]

p=0.427

NA


123

For the SDI malignancy subscore there was no significant difference (p=0.389) between

belimumab and SoC in the change from baseline score when controlling for both SDI baseline

score and decade of entry. See Table 139.

Table 139. SDI malignancy subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0199 (0.0094)

95% CI: [0.0013 ; 0.0384] p=0.036

0.0199 (0.0138) 95% CI: [-0.0072 ; 0.0469]

p=0.150

0.0276 (0.0119) 95% CI: [0.0043 ; 0.0510]

p=0.021

Belimumab 0.0060 (0.0070)

95% CI: [-0.0077 ; 0.0197] p=0.389

0.0060 (0.0042) 95% CI: [-0.0023 ; 0.0143]

p=0.154

0.0061 (0.0086) 95% CI: [-0.0108 ; 0.0231]

p=0.478

Baseline

SDI = 1

0.0122 (0.0066) 95% CI: [-0.0008 ; 0.0252]

p=0.065

0.0122 (0.0104) 95% CI: [-0.0082 ; 0.0327]

p=0.241

-0.0094 (0.0101) 95% CI: [-0.0292 ; 0.0104]

p=0.351

Baseline

SDI = 2+

-0.0027 (0.0079) 95% CI: [-0.0183 ; 0.0128]

p=0.731

-0.0027 (0.0027) 95% CI: [-0.0080 ; 0.0026]

p=0.314

-0.0104 (0.0107) 95% CI: [-0.0315 ; 0.0107]

p=0.334

Entry Decade

2000

-0.0336 (0.0101) 95% CI: [-0.0534 ; -0.0137]

p<0.001

-0.0336 (0.0234) 95% CI: [-0.0795 ; 0.0123]

p=0.152

-0.0245 (0.0132) 95% CI: [-0.0505 ; 0.0015]

p=0.064

Entry Decade

2010

-0.0276 (0.0105) 95% CI: [-0.0482 ; -0.0069]

p=0.009

-0.0276 (0.0190) 95% CI: [-0.0647 ; 0.0096]

p=0.146

-0.0240 (0.0209) 95% CI: [-0.0651 ; 0.0171]

p=0.252

Final Year 0.0017 (0.0010)

95% CI: [-0.0002 ; 0.0036] p=0.076

0.0017 (0.0016) 95% CI: [-0.0014 ; 0.0048]

p=0.286

NA


When controlling for decade of entry, for the SDI musculoskeletal subscore belimumab subjects

had a significantly (p<0.001) smaller increase from baseline score compared to SoC subjects.

See Table 140. The treatment coefficient (-0.2150) for the 5th year change from baseline is

within the 95% confidence interval (-0.2525 ; -0.0769) of the treatment coefficient determined

when using a subject’s last visit.

124

Table 140. SDI musculoskeletal system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.1967 (0.0492)

95% CI: [0.1001 ; 0.2933] p<0.001

0.1967 (0.0705) 95% CI: [0.0585 ; 0.3349]

p=0.005

0.3256 (0.0721) 95% CI: [0.1838 ; 0.4674]

p<0.001

Belimumab -0.1647 (0.0367)

95% CI: [-0.2368 ; -0.0926] p<0.001

-0.1647 (0.0448) 95% CI: [-0.2525 ; -0.0769]

p<0.001

-0.2150 (0.0549) 95% CI: [-0.3229 ; -0.1072]

p<0.001

Entry Decade

2000

-0.0954 (0.0534) 95% CI: [-0.2002 ; 0.0094]

p=0.074

-0.0954 (0.0922) 95% CI: [-0.2761 ; 0.0853]

p=0.301

-0.0482 (0.0837) 95% CI: [-0.2128 ; 0.1164]

p=0.565

Entry Decade

2010

-0.1845 (0.0554) 95% CI: [-0.2933 ; -0.0758]

p<0.001

-0.1845 (0.0773) 95% CI: [-0.3360 ; -0.0330]

p=0.017

-0.1906 (0.1329) 95% CI: [-0.4519 ; 0.0708]

p=0.152

Final Year 0.0177 (0.0051)

95% CI: [0.0078 ; 0.0277] p<0.001

0.0177 (0.0071) 95% CI: [0.0039 ; 0.0316]

p=0.012

NA

Abbreviations: CI, confidence interval; OLS, ordinary least squares; NA, not applicable; PS, propensity score; SDI, SLICC/ACR Damage Index; SE, standard error

For the SDI neuropsychiatric system subscore there was no significant difference (p=0.556)

between belimumab and SoC in the change from baseline score when controlling for both SDI

baseline score and decade of entry. See Table 141.

125

Table 141. SDI neuropsychiatric system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0756 (0.0261)

95% CI: [0.0244 ; 0.1269] p=0.004

0.0756 (0.0428) 95% CI: [-0.0082 ; 0.1595]

p=0.077

0.0704 (0.0454) 95% CI: [-0.0188 ; 0.1596]

p=0.122

Belimumab -0.0113 (0.0192)

95% CI: [-0.0491 ; 0.0265] p=0.556

-0.0113 (0.0170) 95% CI: [-0.0446 ; 0.0219]

p=0.504

-0.0621 (0.0329) 95% CI: [-0.1269 ; 0.0027]

p=0.060

Baseline

SDI = 1

0.0401 (0.0183) 95% CI: [0.0042 ; 0.0760]

p=0.029

0.0401 (0.0240) 95% CI: [-0.0070 ; 0.0872]

p=0.095

0.0014 (0.0384) 95% CI: [-0.0740 ; 0.0769]

p=0.970

Baseline

SDI = 2+

-0.0049 (0.0218) 95% CI: [-0.0478 ; 0.0380]

p=0.823

-0.0049 (0.0186) 95% CI: [-0.0413 ; 0.0315]

p=0.792

-0.0040 (0.0410) 95% CI: [-0.0847 ; 0.0766]

p=0.922

Entry Decade

2000

-0.0952 (0.0279) 95% CI: [-0.1501 ; -0.0404]

p<0.001

-0.0952 (0.0472) 95% CI: [-0.1878 ; -0.0027]

p=0.044

0.0201 (0.0504) 95% CI: [-0.0790 ; 0.1191]

p=0.691

Entry Decade

2010

-0.0804 (0.0290) 95% CI: [-0.1374 ; -0.0234]

p=0.006

-0.0804 (0.0468) 95% CI: [-0.1720 ; 0.0113]

p=0.086

-0.0077 (0.0797) 95% CI: [-0.1645 ; 0.1491]

p=0.923

Final Year 0.0078 (0.0027)

95% CI: [0.0026 ; 0.0130] p=0.004

0.0078 (0.0033) 95% CI: [0.0014 ; 0.0142]

p=0.017

NA


For the SDI ocular subscore there was no significant difference (p=0.116) between belimumab

and SoC in the change from baseline score. See Table 142.

126

Table 142. SDI ocular system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept -0.0094 (0.0220)

95% CI: [-0.0526 ; 0.0339] p=0.671

-0.0094 (0.0181) 95% CI: [-0.0448 ; 0.0261]

p=0.604

0.0884 (0.0213) 95% CI: [0.0466 ; 0.1302]

p<0.001

Belimumab -0.0313 (0.0199)

95% CI: [-0.0705 ; 0.0078] p=0.116

-0.0313 (0.0192) 95% CI: [-0.0690 ; 0.0063]

p=0.103

-0.0111 (0.0301) 95% CI: [-0.0702 ; 0.0481]

p=0.714

Final Year 0.0181 (0.0033)

95% CI: [0.0116 ; 0.0245] p<0.001

0.0181 (0.0043) 95% CI: [0.0097 ; 0.0264]

p<0.001

NA


When controlling for decade of entry, for the SDI peripheral vascular system subscore there was



Table 143. SDI peripheral vascular system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0611 (0.0232)

95% CI: [0.0155 ; 0.1066] p=0.009

0.0611 (0.0404) 95% CI: [-0.0182 ; 0.1403]

p=0.131

0.0233 (0.0287) 95% CI: [-0.0331 ; 0.0796]

p=0.417

Belimumab -0.0223 (0.0173)

95% CI: [-0.0563 ; 0.0117] p=0.198

-0.0223 (0.0177) 95% CI: [-0.0570 ; 0.0124]

p=0.208

-0.0127 (0.0218) 95% CI: [-0.0556 ; 0.0301]

p=0.559

Entry Decade 2000

-0.0607 (0.0252) 95% CI: [-0.1101 ; -0.0112]

p=0.016

-0.0607 (0.0512) 95% CI: [-0.1610 ; 0.0396]

p=0.236

0.0175 (0.0333) 95% CI: [-0.0478 ; 0.0829]

p=0.598

Entry Decade 2010

-0.0694 (0.0261) 95% CI: [-0.1206 ; -0.0181]

p=0.008

-0.0694 (0.0441) 95% CI: [-0.1558 ; 0.0171]

p=0.116

-0.0218 (0.0528) 95% CI: [-0.1257 ; 0.0820]

p=0.679

Final Year 0.0058 (0.0024)

95% CI: [0.0011 ; 0.0105] p=0.015

0.0058 (0.0031) 95% CI: [-0.0003 ; 0.0120]

p=0.063

NA


127

When controlling for both SDI baseline score and decade of entry, for the SDI pulmonary

system subscore there was no significant difference (p=0.667) between belimumab and SoC in

the change from baseline score. See Table 144.

Table 144. SDI pulmonary system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0585 (0.0189)

95% CI: [0.0213 ; 0.0957] p=0.002

0.0585 (0.0286) 95% CI: [0.0024 ; 0.1146]

p=0.041

0.0264 (0.0222) 95% CI: [-0.0172 ; 0.0700]

p=0.235

Belimumab -0.0060 (0.0140)

95% CI: [-0.0334 ; 0.0214] p=0.667

-0.0060 (0.0115) 95% CI: [-0.0285 ; 0.0165]

p=0.601

-0.0316 (0.0161) 95% CI: [-0.0633 ; 0.0001]

p=0.050

Baseline

SDI = 1

-0.0031 (0.0133) 95% CI: [-0.0291 ; 0.0230]

p=0.816

-0.0031 (0.0125) 95% CI: [-0.0276 ; 0.0215]

p=0.806

-0.0158 (0.0188) 95% CI: [-0.0527 ; 0.0211]

p=0.400

Baseline

SDI = 2+

-0.0193 (0.0158) 95% CI: [-0.0504 ; 0.0118]

p=0.224

-0.0193 (0.0077) 95% CI: [-0.0344 ; -0.0041]

p=0.013

0.0007 (0.0200) 95% CI: [-0.0387 ; 0.0401]

p=0.972

Entry Decade

2000

-0.0468 (0.0203) 95% CI: [-0.0866 ; -0.0071]

p=0.021

-0.0468 (0.0313) 95% CI: [-0.1082 ; 0.0145]

p=0.135

0.0086 (0.0246) 95% CI: [-0.0398 ; 0.0570]

p=0.726

Entry Decade

2010

-0.0395 (0.0210) 95% CI: [-0.0808 ; 0.0019]

p=0.061

-0.0395 (0.0314) 95% CI: [-0.1010 ; 0.0221]

p=0.209

-0.0186 (0.0390) 95% CI: [-0.0953 ; 0.0580]

p=0.633

Final Year 0.0008 (0.0019)

95% CI: [-0.0030 ; 0.0046] p=0.683

0.0008 (0.0018) 95% CI: [-0.0028 ; 0.0044]

p=0.669

NA


For the SDI renal system subscore there was no significant difference (p=0.840) between


128

Table 145. SDI renal system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0156 (0.0149)

95% CI: [-0.0136 ; 0.0448] p=0.295

0.0156 (0.0102) 95% CI: [-0.0044 ; 0.0355]

p=0.126

0.0221 (0.0087) 95% CI: [0.0050 ; 0.0392]

p=0.011

Belimumab -0.0027 (0.0134)

95% CI: [-0.0291 ; 0.0237] p=0.840

-0.0027 (0.0134) 95% CI: [-0.0289 ; 0.0235]

p=0.839

-0.0166 (0.0123) 95% CI: [-0.0407 ; 0.0076]

p=0.178

Final Year 0.0012 (0.0022)

95% CI: [-0.0032 ; 0.0055] p=0.594

0.0012 (0.0017) 95% CI: [-0.0022 ; 0.0046]

p=0.492

NA


When controlling for both SDI baseline score and decade of entry, for the SDI skin system

subscore there was no significant difference (p=0.152) between belimumab and SoC in the

change from baseline score. See Table 146.

129

Table 146. SDI skin system subscore change from baseline

Variable


[95% CI] P value


[95% CI] P value


[95% CI] P value

Intercept 0.0733 (0.0249)

95% CI: [0.0243 ; 0.1222] p=0.003

0.0733 (0.0486) 95% CI: [-0.0220 ; 0.1685]

p=0.132

0.0619 (0.0306) 95% CI: [0.0018 ; 0.1220]

p=0.044

Belimumab -0.0264 (0.0184)

95% CI: [-0.0625 ; 0.0097] p=0.152

-0.0264 (0.0177) 95% CI: [-0.0610 ; 0.0082]

p=0.135

-0.0777 (0.0222) 95% CI: [-0.1214 ; -0.0341]

p<0.001

Baseline

SDI = 1

-0.0237 (0.0175) 95% CI: [-0.0580 ; 0.0106]

p=0.175

-0.0237 (0.0122) 95% CI: [-0.0476 ; 0.0002]

p=0.052

-0.0438 (0.0259) 95% CI: [-0.0946 ; 0.0071]

p=0.091

Baseline

SDI = 2+

-0.0337 (0.0209) 95% CI: [-0.0747 ; 0.0073]

p=0.107

-0.0337 (0.0107) 95% CI: [-0.0548 ; -0.0126]

p=0.002

-0.0267 (0.0276) 95% CI: [-0.0811 ; 0.0276]

p=0.334

Entry Decade

2000

-0.0368 (0.0267) 95% CI: [-0.0892 ; 0.0156]

p=0.169

-0.0368 (0.0521) 95% CI: [-0.1388 ; 0.0653]

p=0.480

0.0303 (0.0339) 95% CI: [-0.0365 ; 0.0970]

p=0.373

Entry Decade

2010

-0.0598 (0.0277) 95% CI: [-0.1142 ; -0.0054]

p=0.031

-0.0598 (0.0482) 95% CI: [-0.1543 ; 0.0347]

p=0.215

-0.0366 (0.0537) 95% CI: [-0.1423 ; 0.0690]

p=0.496

Final Year 0.0019 (0.0025)

95% CI: [-0.0030 ; 0.0069] p=0.445

0.0019 (0.0024) 95% CI: [-0.0027 ; 0.0066]

p=0.414

NA



The BEL 112234 dataset did not contain longitudinal SLEDAI scores. Therefore, this analysis

could not be undertaken.


interval

The BEL 112234 dataset did not contain enough longitudinal concomitant medication data for

this analysis to be feasible. Therefore, this analysis could not be undertaken.

130

6.3 Diagnostics

6.3.1 Baseline characteristics of unmatched study arms


Comparison of baseline characteristics was performed as part of the PSM process. For subjects

with at least 5 years of follow-up almost all means of baseline characteristics were significantly

different between study arms. See Table 10. The high degree of dissimilarity between study

arms was further reflected by the large percent bias seen across nearly all covariates.

The results are similar when comparing subjects with ≥ 1 year of follow-up. See Table 14. All

but one of the baseline averages were significantly different between study arms. The high

degree of dissimilarity between study arms was further reflected by the large percent bias seen

across nearly all covariates.


Comparison of baseline characteristics was performed as part of the PSM process. For subjects

with at least 5 years of follow-up almost all means of baseline characteristics were significantly

different between study arms. See Table 81. The high degree of dissimilarity between study

arms was further reflected by the large percent bias seen across all covariates.

The results were similar for subjects with ≥ 1 year of follow-up. See Table 85. All but three of the

baseline averages were significantly different between study arms. The high degree of

dissimilarity between study arms was further reflected by the large percent bias seen across

nearly all covariates.

6.3.2 Baseline characteristics of matched samples


Comparison of baseline characteristics of matched samples was performed as part of the PSM

process to confirm the statistical validity of the matched samples. For subjects with at least 5

years of follow-up there were no statistically significant differences in any of the means of

baseline characteristics. See Table 11. Bias was less than 10% for all characteristics.

The results were similar for subjects with ≥ 1 year of follow-up. See Table 15 . After matching,

there were no statistically significant differences in any of the means of baseline characteristics.

Bias was less than 10% for all characteristics, with the largest being 5.2% for dyslipidemia.

131


Comparison of baseline characteristics of matched samples was performed as part of the PSM

process to confirm the statistical validity of the matched samples. For subjects with at least 5

years of follow-up, there were no statistically significant differences in any of the means of

baseline characteristics. Further, all bias percentages were less than 5% except for

immunosuppressive use (7.8%) and antimalarial use (14.7%). See Table 82.

The results were similar for subjects with ≥ 1 year of follow-up. After matching, there were no

statistically significant differences in any of the means of baseline characteristics. Bias was less

than 10% for all characteristics, with 9.8% for baseline SDI score equals one and less than 5%

for all but four other characteristics of which the largest was 6.3%. See Table 86.



In BEL112233 SDI “annual” visits could be determined by a number of factors. Likewise, visits

for TLC patients were not strictly scheduled and could vary from patient to patient. Length of

collection of SLEDAI also varied (see Section 5.4 above). Therefore, an analysis was performed

of the time from baseline to 5th year “annual” visit for both SDI and AMS. The distributions of

time from baseline to the 5th year observations are displayed in Figure 5.

132

Figure 5. BEL112233 and TLC 5th year distributions of years from baseline

A paired t-test was used to test for a difference between matched pairs’ 5th year days from

baseline. For the SDI timeline, on average, there was no significant difference (p=0.124)

between matched subjects in their elapsed time at the 5th year observation. Similarly, on

average, for the AMS timeline there was no significant difference (p=0.941) between matched

pairs in the number of days from baseline till the 5th year visit. See Table 147.

Table 147. Paired t-test for matched subjects’ 5th year days from baseline

SDI 5th year visit Average Difference (SE)

[95% CI] P value

AMS 5th year visit Average Difference (SE)

[95% CI] P value

10.19 (6.57) 95% CI: [-2.85 ; 23.24]

p=0.124

-0.42 (5.67) 95% CI: [-11.68 ; 10.84]

p=0.941

Abbreviations: AMS, adjusted mean SLEDAI; CI, confidence interval; SDI, SLICC/ACR Damage Index; SE, standard error; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index

133

6.3.3.2 Pooled BLISS and TLC

In the pooled dataset SDI “annual” visits could be determined by a number of factors. Likewise,

visits for TLC patients were not strictly scheduled and could vary from patient to patient (see

Section 5.4 above). Therefore, an analysis was performed of the time from baseline to 5th year

“annual” visit. The distributions of time from baseline to the 5th year observation are displayed in

Figure 6.

Figure 6. Pooled BLISS and TLC 5th year distributions of years from baseline

A paired t-test was used to test for a difference between matched pairs’ 5th year days from

baseline. For the SDI timeline, on average, there was no significant difference (p=0.766)

between matched subjects in their elapsed time at the 5th year observation. See Table 148.

134

Table 148. Paired t-test for matched subjects’ 5th year days from baseline

SDI 5th year visit Average Difference (SE)

[95% CI] P value

-1.27 (4.27)

95% CI: [-9.70 ; 7.16]

p=0.766


6.3.4 Patients withdrawing from US LTE, Pooled LTE and TLC cohorts

For the US LTE and TLC cohorts, higher proportions were seen among subjects who withdrew

compared to subjects with at least 5 years of follow-up in the following categories: Asian or

other race (23.1% versus 18.4%), smokers (22.3% versus 17.0%), history of proteinuria (28.4%

versus 24.7%) and any steroid use (66.8% versus 61.3%). Lower proportions were seen in the

following categories: belimumab use (28.4% versus 33.9%), antimalarial use (54.6% versus

59.3%) and dyslipidemia (28.4% versus 32.6%). See Table 150. Two-proportion z-tests were

used to test for differences in baseline characteristics. There was no statistically significant

difference in binary baseline characteristics. The proportion of smokers was marginally

significant (p=0.082).

Table 149. Comparison of US LTE and TLC baseline characteristics between patients

with 5 years follow-up and discontinuers

Withdrew (N=229) At least 5 years (N=599) p

Belimumab use 65 (28.4%) 203 (33.9%) 0.130

Female 207 (90.4%) 540 (90.2%) 0.916

Black 35 (15.3%) 99 (16.5%) 0.664

Asian/Other race 53 (23.1%) 110 (18.4%) 0.122

Smoker 51 (22.3%) 102 (17.0%) 0.082

Hypertension 93 (40.6%) 257 (42.9%) 0.550

Proteinuria 65 (28.4%) 148 (24.7%) 0.279

Antimalarial use 125 (54.6%) 355 (59.3%) 0.222

Immunosuppressant use 89 (38.9%) 233 (38.9%) 0.993

Steroid Use 153 (66.8%) 367 (61.3%) 0.140

Dyslipidemia 65 (28.4%) 195 (32.6%) 0.248

135

For the pooled LTE and TLC cohorts, higher proportions were seen among subjects who

withdrew compared to subjects with at least 5 years of follow-up in the following categories:

belimumab use (69.0% versus 59.7%), female (94.2% versus 91.1%), immunosuppressant use

(44.0% versus 39.8%) and any steroid use (81.5% versus 74.5%). Lower proportions were seen

in the following categories: black race (7.5% versus 11.0%), hypertension (36.0% versus

40.4%), antimalarial use (58.5% versus 62.6%) and dyslipidemia (21.7% versus 30.2%). See

Table 149. Two-proportion z-tests were used to test for differences in baseline characteristics.

For subjects who did not complete at least five years of follow-up, the proportion that used

belimumab, the proportion that were women, and the proportion who used steroids at baseline

were significantly greater than seen for subjects with at least 5 years of follow-up (p<0.001,

p=0.032 and p=0.002, respectively). Subjects without 5 years of follow-up also had significantly

lower proportions of black subjects and subjects with dyslipidemia at baseline (p=0.030 and

p<0.001, respectively). The proportion with hypertension was marginally significant (p=0.095).

Table 150. Comparison of pooled LTE and TLC baseline characteristics between patients

with 5 years follow-up and discontinuers

Withdrew (N=520) At least 5 years (N=991) p

Belimumab use 359 (69.0%) 592 (59.7%) <0.001

Female 490 (94.2%) 903 (91.1%) 0.032

Black 39 (7.5%) 109 (11.0%) 0.030

Asian/Other race 202 (38.8%) 371 (37.4%) 0.592

Hypertension 187 (36.0%) 400 (40.4%) 0.095

Proteinuria 115 (22.1%) 223 (22.5%) 0.864

Antimalarial use 304 (58.5%) 620 (62.6%) 0.120

Immunosuppressant use 229 (44.0%) 394 (39.8%) 0.108

Steroid Use 424 (81.5%) 738 (74.5%) 0.002

Dyslipidemia 113 (21.7%) 299 (30.2%) <0.001

For the US LTE and TLC cohorts, within the SDI organ system subscores the proportions of

various scores among subjects who withdrew compared to subjects with at least 5 years of

follow-up were relatively similar. There was a slight difference in the musculoskeletal subgroup

where subjects who withdrew had a lower proportion with no musculoskeletal damage (84.3%

versus 87.3%). See Table 151.

136

Table 151. US LTE and TLC baseline SDI organ system subscore counts

SDI Group Counts by baseline scores

0 1 2 3 4 ≥5

Total Score

Withdrew 154

(67.2%) 38

(16.6%) 22

(9.6%) 4

(1.7%) 4

(1.7%) 7

(3.06%)

At least 5 years 390

(65.1%) 112

(18.7%) 55

(9.2%) 21

(3.5%) 11

(1.8%) 10

(1.67%)

Ocular

Withdrew 212

(92.6%) 17

(7.4%) NA NA NA NA


(95.3%) 27

(4.5%) 1

(0.2%) NA NA NA

Neuropsychiatric

Withdrew 207

(90.4%) 16

(7.0%) 3

(1.3%) 2

(0.9%) 1

(0.4%) NA


(89.1%) 50

(8.3%) 13

(2.2%) 2

(0.3%) NA NA

Renal

Withdrew 225

(98.3%) 4

(1.7%) NA NA NA NA


(97.0%) 17

(2.8%) 1

(0.2%) NA NA NA

Pulmonary

Withdrew 225

(98.3%) 4

(1.7%) NA NA NA NA


(98.0%) 10

(1.7%) 2

(0.3%) NA NA NA

Cardiovascular

Withdrew 225

(98.3%) 3

(1.3%) 1

(0.4%) NA NA NA


(96.0%) 22

(3.7%) 2

(0.3%) NA NA NA

Peripheral Vascular

Withdrew 224

(97.8%) 4

(1.7%) NA

1 (0.4%)

NA NA


(97.7%) 14

(2.3%) NA NA NA NA

Gastrointestinal

Withdrew 222

(96.9%) 7

(3.1%) NA NA NA NA


(96.3%) 21

(3.5%) 1

(0.2%) NA NA NA

137

Musculoskeletal

Withdrew 193

(84.3%) 21

(9.2%) 10

(4.4%) 3

(1.3%) 1

(0.4%) 1

(0.4%)


(87.3%) 45

(7.5%) 27

(4.5%) 4

(0.7%) NA NA

Skin

Withdrew 216

(94.3%) 13

(5.7%) NA NA NA NA


(94.3%) 32

(5.3%) 2

(0.3%) NA NA NA

Gonadal

Withdrew 226

(98.7%) 3

(1.3%) NA NA NA NA


(98.7%) 8

(1.3%) NA NA NA NA

Diabetes

Withdrew 226

(98.7%) 3

(1.3%) NA NA NA NA


(97.2%) 17

(2.8%) NA NA NA NA

Malignancy

Withdrew 227

(99.1%) 2

(0.9%) NA NA NA NA


(99.5%) 3

(0.5%) NA NA NA NA

Abbreviations: NA, not applicable; SDI, SLICC/ACR Damage Index

For the pooled LTE and TLC cohorts, within the SDI organ system subscores the proportions of

various scores among subjects who withdrew compared to subjects with at least 5 years of

follow-up were relatively similar. The largest difference was in the ocular subgroup where

subjects who withdrew had a lower proportion with no ocular damage (91.9% versus 95.2%).

See Table 152.

Table 152. Pooled LTE and TLC baseline SDI organ system subscore counts

SDI Group Counts by baseline scores

0 1 2 3 4 ≥5

Total Score

Withdrew 329

(63.3%) 110

(21.2%) 50

(9.6%) 13

(2.5%) 8

(1.5%) 10

(1.92%)


(65.6%) 195

(19.7%) 82

(8.3%) 37

(3.7%) 15

(1.5%) 12

(1.21%)

Ocular

Withdrew 478

(91.9%) 41

(7.9%) 1

(0.2%) NA NA NA


(95.2%) 47

(4.7%) 1

(0.1%) NA NA NA

Neuropsychiatric Withdrew 473

(91.0%) 37

(7.1%) 6

(1.2%) 2

(0.4%) 2

(0.4%) NA

138


(91.3%) 66

(6.7%) 18

(1.8%) 2

(0.2%) NA NA

Renal

Withdrew 512

(98.5%) 8

(1.5%) NA NA NA NA


(97.8%) 21

(2.1%) 1

(0.1%) NA NA NA

Pulmonary

Withdrew 510

(98.1%) 9

(1.7%) 1

(0.2%) NA NA NA


(97.7%) 20

(2.0%) 2

(0.2%) 1

(0.1%) NA NA

Cardiovascular

Withdrew 501

(96.3%) 16

(3.1%) 3

(0.6%) NA NA NA


(96.0%) 36

(3.6%) 4

(0.4%) NA NA NA

Peripheral Vascular

Withdrew 504

(96.9%) 14

(2.7%) NA

2 (0.4%)

NA NA


(97.6%) 22

(2.2%) 1

(0.1%) 1

(0.1%) NA NA

Gastrointestinal

Withdrew 503

(96.7%) 16

(3.1%) 1

(0.2%) NA NA NA


(96.5%) 33

(3.3%) 2

(0.2%) NA NA NA

Musculoskeletal

Withdrew 456

(87.7%) 43

(8.3%) 14

(2.7%) 5

(1.0%) 1

(0.2%) 1

(0.2%)


(87.3%) 86

(8.7%) 35

(3.5%) 5

(0.5%) NA NA

Skin

Withdrew 477

(91.7%) 40

(7.7%) 3

(0.6%) NA NA NA


(93.8%) 58

(5.9%) 3

(0.3%) NA NA NA

Gonadal

Withdrew 510

(98.1%) 10

(1.9%) NA NA NA NA


(98.4%) 16

(1.6%) NA NA NA NA

Diabetes

Withdrew 512

(98.5%) 8

(1.5%) NA NA NA NA


(97.7%) 23

(2.3%) NA NA NA NA

Malignancy

Withdrew 517

(99.4%) 3

(0.6%) NA NA NA NA


(99.5%) 5

(0.5%) NA NA NA NA

139

For the US LTE and TLC cohorts, the means and medians for numerical baseline

characteristics among subjects who withdrew compared to subjects with at least 5 years of

follow-up were relatively similar. The largest difference was seen in median age, which was 3

years younger for subjects who withdrew. See Table 153. Welch’s t-test was used for testing the

difference in means, and Mood’s median test, utilizing a two-tail Fisher exact test, was used to

test for the difference in medians. Only the mean baseline year was found to be significantly

different (p=0.031) with subjects who withdrew beginning a year earlier on average. The

difference in median age was marginally significant (p=0.062).

Table 153. Mean and median of US LTE and TLC baseline variables

Mean Median

Variable Withdrew At least 5 Years p Withdrew At least 5 Years p

Age 38.04 38.95 0.393 36.01 39.05 0.062

Disease duration 6.26 6.55 0.580 4.27 3.99 0.877

SLEDAI 9.18 9.24 0.841 8.00 8.00 0.580

ACR criteria 5.90 5.74 0.168 6.00 6.00 0.132

Baseline year 2003 2004 0.031 2007 2006 0.816

Steroid dose 12.15 11.04 0.374 5.00 5.00 0.641

Abbreviations: ACR, American College of Rheumatology; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index

For the pooled LTE and TLC cohorts, the means and medians for numerical baseline

characteristics among subjects who withdrew compared to subjects with at least 5 years of

follow-up were relatively similar for disease duration, SLEDAI score and steroid dose at

baseline. See Table 154. Welch’s t-test was used for testing the difference in means, and

Mood’s median test, utilizing a two-tail Fisher exact test, was used to test for the difference in

medians. The mean and median for baseline age where significantly lower among subjects with

less than five years of follow-up compared to subjects with at least five years (p=0.011 and

p<0.001, respectively). Also, the mean baseline ACR criteria score was found to be significantly

higher (p=0.049) for subjects who withdrew. Finally, although the mean baseline year was not

significantly different and the median values are only a year apart, the odds of being below the

pooled median baseline year were significantly lower (p=0.006) for subjects who withdrew.

140

Table 154. Mean and median of pooled LTE and TLC baseline variables

Mean Median

Variable Withdrew At least 5 Years p Withdrew At least 5 Years p

Age 36.90 38.61 0.011 34.85 39.00 <0.001

Disease duration 6.60 6.33 0.463 4.65 4.20 0.130

SLEDAI 9.00 8.82 0.444 8.00 8.00 0.186

ACR criteria 5.96 5.81 0.049 6.00 6.00 0.141

Baseline year 2006 2005 0.254 2008 2007 0.006

Steroid dose 11.93 11.23 0.308 10.00 7.50 0.105

Abbreviations: ACR, American College of Rheumatology; SLEDAI, Systemic Lupus Erythematosus Disease Activity Index

Time to event analyses were used to test for differences in clinical outcomes between subjects

who completed at least five years of follow-up versus those who withdrew prior to five years. All

study participants from both cohorts were included if their baseline date was between the start

of 1990 and the end of 2010. In all the time to event analyses statistically significant prediction

variables and a binary indicator variable for withdrawing prior to five years were included as

covariates.

Based on the results from Section 6.1.3 a constant hazard rate was assumed for the time to first

increase in total SDI score. The only predictor was a binary indicator for treatment with

belimumab.

When controlling for treatment with belimumab, there was no significant difference (p=0.396) in

time to first increase in total SDI score between those who withdrew versus those who

completed at least 5 years for the US LTE and TLC cohorts. See Table 155.

Table 155. US LTE and TLC proportional hazards model of time to first change in total

SDI score, exponential distribution

Variable Coefficient SE p

Intercept -2.2335 0.0761 <0.001

Belimumab -0.7798 0.1565 <0.001

Subjects who withdrew 0.1358 0.1601 0.396

Abbreviations: SDI, SLICC/ACR Damage Index; SE, standard error

For the pooled LTE and TLC cohorts, when controlling for treatment with belimumab, subjects

who withdrew had a significantly higher rate of organ damage progression (p<0.001) compared

to subjects who completed at least 5 years. See Table 156

141

Table 156. Pooled LTE and TLC proportional hazards model of time to first change in

total SDI score, exponential distribution


Intercept -2.4850 0.0644 <0.001

Belimumab -1.0450 0.1062 <0.001

Subjects who withdrew 0.4430 0.1256 <0.001


A lognormal accelerated failure time model was found to provide the best fit for predicting the

time to mild/moderate flares. The significant covariates for time to mild/moderate flare were

AMS and treatment duration. The relationship between mild/moderate flares and AMS was non-

linear, and thus AMS was transformed using the inverse hyperbolic sine function:

IHS(𝑥) = log(𝑥 + (𝑥2 + 1)1 2⁄ )

Robust sandwich variance estimators were used due to multiple observations per subject.

For the US LTE and TLC cohorts, after controlling for AMS and treatment duration there was no

significant difference (p=0.689) in time to mild/moderate flare between those who withdrew

versus those who completed at least 5 years. See Table 157.

Table 157. Mild/moderate flares (lognormal accelerated failure time model)


Intercept 0.4538 0.0832 <0.001

Inverse Sine (AMS) -0.3618 0.0307 <0.001

Log(Time) 0.0910 0.0309 0.003

Subjects who withdrew 0.0224 0.0561 0.689

Log(scale) -0.0837 0.0237 <0.001

Abbreviations: AMS, adjusted mean SLEDAI; SE, standard error

A lognormal accelerated failure time model was found to provide the best fit for predicting the

time to severe flares. Significant covariates for time to severe flare were: AMS, a binary

indicator for immunosuppressant use and the treatment duration. The relationship between

severe flares and AMS is non-linear, and thus AMS is transformed using the inverse hyperbolic

sine function. Robust sandwich variance estimators were used due to multiple observations per

subject.

142

For the US LTE and TLC cohorts, after controlling for AMS, immunosuppressant use and

treatment duration there was a marginally significant difference (p=0.069) in time to severe flare

between those who withdrew versus those who completed at least 5 years. See Table 158.

Subjects who withdrew tended to experience severe flares within a shorter time interval

compared to subjects who completed at least 5 years.

Table 158. Severe flares (lognormal accelerated failure time model)


Intercept 1.7935 0.1070 <0.001

Inverse Sine (AMS) -0.5271 0.0380 <0.001

Immunosuppressant use -0.3875 0.0580 <0.001

Log(Time) 0.1217 0.0377 0.001

Subjects who withdrew -0.1220 0.0670 0.069

Log(scale) -0.0165 0.0232 0.477


6.3.5 BLISS US LTE and pooled LTE subjects randomized to SoC

Using the PS matched samples for patients with at least one year of follow-up, SoC treatments

in TLC and the BLISS randomized clinical trial were compared using Fisher’s exact test to test

for independence in total SDI score change after 76 weeks from baseline for the US LTE and

either 52 or 76 weeks for the pooled LTE.

There were 54 matched BEL112233 subjects who were randomized to placebo in the US LTE

parent study. Their TLC counterpart’s 76th week visit was the visit closest to 76 weeks from

baseline and restricted to between 52 weeks and 100 weeks. One TLC subject did not have a

visit in this timeframe and the matched pair was dropped.

For matched US LTE and TLC patients, there was no statistically significant difference

(p=0.197) in the change in total SDI score between the two SoC treatment durations. See Table

159.

Table 159. US LTE and TLC total SDI score change from baseline at 76 weeks

SDI Change Standard of Care

N=53

Belimumab

N=53

0 [n (%)] 42 (79.2%) 49 (92.5%)

+1 [n (%)] 8 (15.1%) 3 (5.7%)

+2 [n (%)] 3 (5.7%) 1 (1.9%)

Fisher’s exact test: p=0.197

Abbreviations: SDI, SLICC/ACR Damage Index

143

There were 75 matched BEL112233 and BEL112234 subjects who were randomized to placebo

in the parent study. Depending on the length of the BLISS patient’s parent study, their TLC

counterpart’s visit was selected from either the visit closest to 52 or 76 weeks from baseline.

The maximum visit lengths from baseline for TLC patients were 71 weeks and 96 weeks,

respectively.

For matched pooled LTE and TLC patients, there was no statistically significant difference

(p=0.204) in the change in total SDI score between the two SoC treatment durations. See Table

160.

Table 160. Pooled LTE and TLC total SDI score change from baseline at 52 or 76 weeks.

SDI Change Standard of Care

N=75

Belimumab

N=75

0 [n (%)] 62 (82.7%) 69 (92.0%)

+1 [n (%)] 10 (13.3%) 4 (5.3%)

+2 [n (%)] 3 (4.0%) 2 (2.7%)

Fisher’s exact test: p=0.204

Abbreviations: SDI, SLICC/ACR Damage Index

6.3.6 Belimumab baseline of US LTE and pooled subjects

Time to event analyses was used to test for differences between patients receiving 10 mg/kg

belimumab, 1mg/kg belimumab, and placebo during the parent trial in clinical outcomes during

the period of the LTE trial. In all these analyses statistically significant prediction variables and a

three-level categorical variable for parent study treatment were included as covariates. The

reference level for parent study treatment was belimumab 10 mg/kg.

Based on the results from section 6.1.3 a constant hazard rate was assumed for the time to first

increase in total SDI score. The exponential model used is shown in Table 21. The only

predictor was a binary indicator for treatment with belimumab. Thus, the only predictor for this

analysis was the parent study treatment covariate.

For US LTE subjects, there was no significant difference (p=0.900) in time to first increase in

total SDI score between parent study subgroups. See Table 161.

144

Table 161. US LTE proportional hazards model of time to first change in total SDI score,



Intercept -3.1707 0.2425 <0.001

Belimumab 1 mg/kg in the parent study 0.0352 0.3198 0.912

Placebo in the parent study 0.1533 0.3483 0.660

Likelihood Ratio Test Test Statistic Degrees of Freedom p

0.2110 2 0.900


For pooled LTE subjects, there was no significant difference (p=0.997) in time to first increase in

total SDI score between parent study subgroups.

Table 162. Pooled LTE proportional hazards model of time to first change in total SDI

score, exponential distribution


Intercept -3.4236 0.1414 <0.001

Belimumab 1 mg/kg in the parent study -0.0073 0.1981 0.971


Likelihood Ratio Test Test Statistic Degrees of Freedom p

0.0057 2 0.997


A lognormal accelerated failure time model was found to provide the best fit for predicting time

to mild/moderate flares. The only significant covariate for time to mild/moderate flare in

BEL112233 patients was AMS. The relationship between mild/moderate flares and AMS is non-

linear, and thus AMS is transformed using the inverse hyperbolic sine function:

IHS(𝑥) = log(𝑥 + (𝑥2 + 1)1 2⁄ )

Robust sandwich variance estimators were used due to multiple observations per subject.

For US LTE subjects, after controlling for AMS, there was no significant difference (p=0.833)

between parent study subgroups in time to mild/moderate flare, Table 163.

145

Table 163. Mild/moderate flares (lognormal accelerated failure time model)


Intercept 0.6623 0.1550 <0.001

Inverse Sine (AMS) -0.4061 0.0610 <0.001



Log(scale) 0.1641 0.0390 <0.001

Wald test for parent study group Test Statistic Degrees of Freedom p

0.2491 2 0.883


A lognormal accelerated failure time model was found to provide the best fit for predicting time

to severe flare. Significant covariates for time to severe flare in BEL112233 patients were:

AMS, a binary indicator for immunosuppressant use and the treatment duration. The

relationship between severe flares and AMS is non-linear, and thus AMS is transformed using

the inverse hyperbolic sine function. Robust sandwich variance estimators were used due to

multiple observations per subject.

For US LTE subjects, after controlling for AMS, immunosuppressant use and treatment duration

there was no significant difference (p=0.818) between parent study subgroups in time to

mild/moderate flare, Table 164.

Table 164. Severe flares (lognormal accelerated failure time model)


Intercept 5.5606 0.7640 <0.001

Inverse Sine (AMS) -1.1145 0.2608 <0.001

Immunosuppressant use -1.0275 0.3923 0.009

Log(Time) 0.0802 0.0655 0.221



Log(scale) 0.5216 0.0909 <0.001

Wald test for parent study group Test Statistic Degrees of Freedom p

0.4015 2 0.818


146

7 DISCUSSION AND CONCLUSIONS

7.1 Discussion

Two LTE trials (BEL112233/NCT00724867 and BEL112234/NCT00712933) have recorded the

effects of long-term belimumab exposure on SLE-related organ damage progression. A pooled

analysis of these studies has previously reported a low incidence of organ damage accrual.1 As

open-label, single-arm trials, however, they could not report a statistical comparison to patients

receiving SoC.

The objective of our study was to make statistical comparisons of organ damage progression in

patients receiving belimumab to patients receiving SoC. Propensity score-based matching was

conducted between patients receiving belimumab in the BEL112233 and pooled LTE datasets

to patients receiving SoC in an external cohort. For this purpose, a systematic literature review

was conducted to identify a cohort with similar clinical characteristics, inclusion and exclusion

criteria, and data collection as the belimumab trials. The cohort identified for comparison was

the TLC.2 Patient characteristics predicting SLE organ damage were selected as the basis for

PSM of LTE and TLC patients.6–9 The primary outcome for our study was the difference in organ

damage progression using the SDI total score over 5 years of treatment with belimumab versus

SoC in patients from the US-based BEL112233 LTE and Canada-based TLC. Our first

secondary outcome was difference in time to first increase in total SDI score. Exploratory

analyses included the same outcomes using the more geographically dispersed pooled

(BEL112233, BEL112234) dataset and the TLC.

There were several limitations to our study. The first is that it was not a randomized controlled

trial. PSM studies match patients on the probability they would receive the treatment being

evaluated. Given a full set of clinical variables that influence choice of treatment, a PSM study

can achieve balance between treated and untreated patient groups on those observed clinical

characteristics. Unlike a randomized controlled trial, it does not – cannot - stochastically balance

unknown variables. A strength of this study is that TLC patients who were otherwise indicated

for treatment did not receive belimumab simply because it was not available, and not due to any

other clinical considerations.

Another limitation was that a moderate number of belimumab patients could be matched. In the

BEL112233 dataset used for the primary and secondary analyses, 99 of 195 (51%) and 179 of

259 (69%) patients receiving belimumab with ≥ 5 years and ≥1 year follow-up, respectively,

could be matched. In the pooled dataset used for the exploratory analyses, 181 of 592 (31%)

147

and 323 of 951 (34%) receiving belimumab with ≥ 5 years and ≥1 year follow-up, respectively,

could be matched. As a test of the impact of this limitation, the primary outcome was re-

estimated using a propensity score weighting technique that included the entire sample. The

results of the PSM and IPSW analyses were quite similar.

The TLC has collected data on its patients for decades while the belimumab trials started in

2007. Therefore, an analysis could be confounded by change in treatment patterns over time.

To minimize that possibility TLC patients with baseline dates before 1990 were excluded. The

decade of baseline date was also tested for significance in all analyses and was included as a

covariate if found statistically significant. We also compared corticosteroid use in the

BEL112233/TLC sample and found no significant difference between patients receiving

belimumab and SoC. It might also be argued that SoC patients may have received a lesser

SoC. This is unlikely as the TLC is sponsored by a university clinician and research group with

over 40 SLE-related peer-reviewed publications.

The frequency of observations was also a limitation. Because one of the LTE parent studies

was 76 weeks in duration and the LTE studies recorded SDI every 48 weeks, the 2nd year for

many LTE patients was a short 24 weeks. All BEL112233 patients originated in that parent

study, so this limitation especially pertains to the primary and secondary analyses. This is

mitigated in non-time-to-event analyses by a visit selection method which selected TLC visits

based on matched LTE patient visits. However, this visit selection method was not performed

for time-to-event analyses.

While pre-specified scheduled visits recorded clinical data of patients in the LTE trials, TLC

patients visited their physicians on an as needed basis plus an annual visit. SDI scores were

recorded only on annual visits. In our analysis “annual” visits were allowed to be as little as 24

weeks and as much as 78 weeks after the preceding “annual” visit.

Despite these differences in frequency of observations, the mean durations between baseline

and 5th year visits in BEL112233 and TLC were not significantly different. The mean durations

between baseline and 5th year visits in pooled LTE and TLC also were not significantly different.

No comparison was made of the timing of visits used for the time-to-event analyses.

7.2 Conclusions

In our PSM US-based sample, US patients receiving belimumab had significantly less SLE-

related organ damage progression over a 5-year period than Canadian patients receiving SoC

as measured by total SDI. This pattern of significantly lower organ damage progression started

148

in the first year and continued every year of the analysis (through year 5). Similarly, the study

also found significantly slower organ damage progression for patients taking belimumab than

SoC in a time to first change in SDI analysis.

The results of our pooled analyses of US and outside US patients were similar. In our PSM

pooled sample, patients receiving belimumab also had significantly less SLE-related organ

damage progression over a 5-year period than Canadian patients receiving SoC. This pattern of

significantly lower organ damage progression started in the first year and continued every year

of the analysis (through year 5). The study also found significantly slower progression for

patients taking belimumab in a time to first change in SDI analysis.

Similar results were seen in analyses of SDI organ system-specific subscores. The low numbers

of events in these analyses, however, suggest these results be regarded with caution.

149

8 References

1. Bruce, I. N. et al. Long-term organ damage accrual and safety in patients with SLE treated

with belimumab plus standard of care. Lupus (2016). doi:10.1177/0961203315625119

2. Medical Decision Modeling Inc. Research of Lupus Cohorts as Comparator Arm in

Belimumab Subcutaneous Cost Effectiveness Model - Internal Document. (2016).

3. Jackson, C. Multi-state models for panel data: the msm package for R. J Stat Soft 38,

(2011).

4. Jackson, C. Multi-state modelling with R: the msm package. (2015).

5. Sutton, E. J., Davidson, J. E. & Bruce, I. N. The systemic lupus international collaborating

clinics (SLICC) damage index: a systematic literature review. Semin. Arthritis Rheum. 43,

352–361 (2013).

6. Alarcón, G. S. et al. Systemic lupus erythematosus in three ethnic groups. XX. Damage as a

predictor of further damage. Rheumatology (Oxford) 43, 202–205 (2004).

7. Petri, M., Purvey, S., Fang, H. & Magder, L. S. Predictors of organ damage in systemic

lupus erythematosus: the Hopkins Lupus Cohort. Arthritis Rheum. 64, 4021–4028 (2012).

8. Becker-Merok, A. & Nossent, H. C. Damage accumulation in systemic lupus erythematosus

and its relation to disease activity and mortality. J. Rheumatol. 33, 1570–1577 (2006).

9. Stoll, T., Sutcliffe, N., Mach, J., Klaghofer, R. & Isenberg, D. A. Analysis of the relationship

between disease activity and damage in patients with systemic lupus erythematosus--a 5-yr

prospective study. Rheumatology (Oxford) 43, 1039–1044 (2004).

10. Impact of Disease Activity on Mortality and Damage Progression in SLE

Patients with Active Disease Despite Standard of Care Using Data from the Toronto Lupus

Cohort HO-14-14537 - Preliminary Report. GlaxoSmithKline internal document (2014).

11. SAS Version 9.4. (SAS Institute, 2013).

12. Coca-Perraillon, M. Local and Global Optimal Propensity Score Matching. (Health Care

Policy Department, Harvard Medical School, 2007).

PPD

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13. Austin, P. C. An Introduction to Propensity Score Methods for Reducing the Effects of

Confounding in Observational Studies. Multivariate Behav Res 46, 399–424 (2011).

Approval

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CONFIDENTIAL

CONFIDENTIAL

Copyright 2017 the GlaxoSmithKline group of companies. All rights reserved. Unauthorized copying or use of this information is prohibited.

HEALTH OUTCOMES STUDY PROTOCOL

UNIQUE IDENTIFIER HO-16-16611/206347

FULL TITLE A propensity score-matched study of systemic lupus erythematosus related organ damage in the BLISS long term extension trials (BEL112233 and BEL112234) and the Toronto Lupus Cohort

ABBREVIATED TITLE A propensity score-matched study of the BLISS long term extension trials vs. the Toronto Lupus Cohort

FINAL PROTOCOL APPROVED

SPONSORSHIP Sponsored

DIVISION Pharma

BUSINESS UNIT Research & Development

DEPARTMENT GHO / Medical Decision Modeling Inc.

STUDY ACCOUNTABLE PERSON

CONTRIBUTING AUTHORS Medical Decision Modeling Inc.

ASSET ID GSK1550188

GSK ASSET Belimumab (Benlysta®)

INDICATION Systemic lupus erythematosus

REVISION CHRONOLOGY:

Version Date Document Type Change(s) since last version

15-JUN-2016 Original n/a

15-NOV-2017 Amendment 01 Updated sensitivity and exploratory analyses to replace use of interim pooled US/OUS LTE dataset (201223) with new pooled dataset of US LTE (BEL112233) and OUS LTE (BEL112234) to be constructed de novo

Description: A propensity score-matched study of the BLISS long term extension trials vs. the Toronto Lupus Cohort

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PROTOCOL SYNOPSIS

Unique Identifier

Abbreviated Title A propensity score-matched study of the BLISS long term extension trials vs. the Toronto Lupus Cohort

GSK Product Belimumab (Benlysta®)

Rationale To demonstrate the reduction in organ damage of long term treatment with belimumab plus standard of care (SoC) versus SoC alone for systemic lupus erythematosus.

Objectives (Primary, Secondary)

To compare the mean change in SDI scores from baseline to year 5 between patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC. To compare the time to first SDI worsening between patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC. To compare the total SDI score at yearly intervals between patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC. To perform MSM transition analyses of SDI independently for the belimumab and SoC groups using the Jackson et al. 2011 methodology based on data from the US BLISS LTE trial (BEL112233) and the TLC. To describe the change from baseline in SDI organ damage system (ocular, neuropsychiatric, renal, pulmonary, cardiovascular, peripheral vascular, gastrointestinal [GI], musculoskeletal, skin, premature gonadal failure, diabetes and malignancy) summarized by year interval of patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC. To compare the difference in mean SLEDAI score from baseline over a 5-year period between patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC. To compare the difference in cumulative corticosteroid usage from baseline over a 5-year period between patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC.

4

As a sensitivity analysis, the primary and secondary objectives above will be retested using a pooled BLISS LTE dataset to be constructed de novo from the BLISS LTE trials (BEL112233 and BEL112234) and the TLC.

Study Design A longitudinal propensity score-matched study comparing individual patients of the BLISS LTE trial(s) to clinically and demographically similar patients in the TLC

Study Population and Sampling Methods

Inclusion criteria:

• Diagnosis of systemic lupus erythematosus (ICD-9 710.0) using ≥ 4 of 11 American College of Rheumatology (ACR) criteria (710.0)

• ≥ 18 years of age

• SELENA SLEDAI/SLEDAI-2K score ≥ 6 at baseline

• Auto-antibody positive (anti-nuclear antibody ≥ 1:80 and/or anti-dsDNA ≥ 30 IU/mL)

Exclusion criteria:

• Active severe lupus nephritis or central nervous system lupus

• Receipt of B cell target therapy at any time

• For TLC patients, previous use of belimumab

Data Source BLISS long term extension trials and Toronto Lupus Cohort

Data Analysis Methods Primary endpoint:

• The difference in change in SDI from baseline to year 5 interval between patients treated with belimumab or SoC, based on the data of the US BLISS LTE trial (BEL112233) and the TLC.

Secondary endpoints: The analysis of all secondary endpoints will use data from the US BLISS LTE trial (BEL112233) for patients treated with belimumab and data from the TLC for patients treated with SoC.

• The difference in time to first SDI worsening between patients treated with belimumab or SoC.

• The change from baseline SDI score by year interval for patients treated with belimumab or SoC.

• The difference in change from baseline SDI score by year interval between patients treated with belimumab or SoC.

• Transition analysis of SDI from baseline over a 5-year period for patients treated with belimumab or SoC.

• Change from baseline in SDI organ damage system (ocular, neuropsychiatric, renal,pulmonary, cardiovascular, peripheral vascular, gastrointestinal [GI], musculoskeletal,skin, premature gonadal failure, diabetes

5

and malignancy) summarized by year interval for patients treated with belimumab or SoC.

• The frequency of increase from baseline in SDI organ damage system subscores between patients treated with belimumab or SoC.

• The difference in mean SLEDAI over the 5-year period.

• The difference in cumulative corticosteroid usage over the 5-year period.

Exploratory endpoints: The analysis of all exploratory endpoints will use the pooled BLISS LTE dataset to be constructed from the BLISS LTE trials (BEL112233 and BEL112234) for patients treated with belimumab and data from the TLC for patients treated with SoC.

• The difference in time to first SDI worsening between patients treated with belimumab or SoC.

• The change from baseline SDI score by year interval for patients treated with belimumab or SoC.

• The difference in change from baseline SDI score by year interval between patients treated with belimumab or SoC.

• Transition analysis of SDI from baseline over a 5 year period for patients treated with belimumab or SoC.

• Change from baseline in SDI organ damage system (ocular, neuropsychiatric, renal, pulmonary, cardiovascular, peripheral vascular, gastrointestina [GI], musculoskeletal, skin, premature gonadal failure, diabetes and malignancy) summarized by year interval for patients treated with belimumab or SoC.

Sample Size and Power Sample size:

LTE TLC

Primary and secondary endpoints

US LTE Sample (≥ 5 years Tx duration) with 2:1 Match

192 384

Exploratory endpoints

Pooled LTE Sample (≥5 years Tx duration) with 1:1 Match*

530 530

Power:

80%, =0.05 to detect a difference of

• Primary endpoint - 0.155 (34%)

• First exploratory endpoint -- 0.113 (27%)

Limitations The primary limitations are the numbers of patients in the BLISS LTE trials, the number of patients in the TLC, and the number of

6

patients with matching characteristics. This limits the power to reach statistically significant conclusions.

Other limitations include:

• Dissimilar data collection cycles, i.e. data in the BLISS LTEs were collected on a pre-defined schedule, while data in the TLC are collected when patients schedule visits.

• The controls will not have been randomly selected from the same population as the BLISS LTE patients. Instead propensity score matching will be used to match belimumab patients with controls based on a number of patient characteristics.

• BLISS patients may have received care from different types of health care systems than external cohort controls.

• SELENA-SLEDAI scores are available for BLISS LTE patients while SLEDAI-2K scores are available for TLC patients. While the components of SELENA-SLEDAI remain the same as the SLEDAI-2K, the definitions of several components are slightly different. The length of assessment was also different in the BLISS trials than in the TLC; up to 10 days prior to a visit in the BLISS trials while up to 30 days prior to a visit in the TLC. A study has indicated that there is minimal difference between 10 and 30 days assessment. The SELENA-SLEDAI is yet to be rigorously validated.

• Patients were free to withdraw from the BLISS studies and from the TLC. This may introduce bias.

• The sensitivity analysis will utilize pooled patient-level data from both BLISS LTE trials (BEL112233 and BEL112234), treating the data as through from one, rather than two, trials. In fact the populations and health systems through which care was given may have been significantly different. The preferred method of aggregating data from multiple trials is meta-analysis. However, using meta-analysis for just two trials would result in loss of power. The pooled dataset will be constructed de novo from the datasets of the BLISS LTE trials (BEL112233 and BEL112234) as part of this analysis.

• The BLISS trials used 48 weeks as a year.

• The PS matching approach to be used only accounts for sample selection bias (aka confounding by indication) for the observed confounders (predictors of organ damage also potentially affecting treatment assignment) included in the PS model. To the extent additional clinically important confounders exist in the data but cannot be observed,

7

sample selection bias cannot be fully addressed using PS adjustment methods.

• The TLC is treated at a single Canadian clinical site while the BLISS BEL112233 LTE trial was conducted at multiple US sites. Differences in outcomes may be confounded by differences in the national health systems. Outcomes may also be confounded by treatment practices specific to the TLC clinical site.

8

TABLE OF CONTENTS 1 INTRODUCTION/BACKGROUND................................................................................................ 11

2 OBJECTIVES ............................................................................................................................ 11

2.1 Primary ................................................................................................................................... 11

2.2 Secondary ............................................................................................................................. 11

2.3 Exploratory ............................................................................................................................ 12

3 RESEARCH METHODOLOGY...................................................................................................... 12

3.1 Study Design ........................................................................................................................... 12

3.2 Study Population ..................................................................................................................... 12

3.2.1 Eligibility Criteria.............................................................................................................. 12

3.2.2 Sampling ......................................................................................................................... 13

3.2.3 Matching Procedure ...................................................................................................... 13

3.2.4 Matching Criteria ........................................................................................................... 15

3.3 Data Source / Data Collection ................................................................................................... 17

3.3.1 TLC Data ....................................................................................................................... 17

3.4 Endpoints .............................................................................................................................. 18

3.4.1 Primary Endpoint ........................................................................................................... 18

3.4.2 Secondary Endpoint(s).................................................................................................. 18

3.4.3 Exploratory Endpoint(s)................................................................................................. 18

3.5 Sample Size / Power Calculations ....................................................................................... 19

3.5.1 Available sample sizes for BLISS LTE and TLC data after propensity score matching ....................................................................................................................... 19

3.5.2 Sample size requirements for “Mean Change from Baseline SDI” endpoint: Alternative treatment effect size assumptions (SDLTE=0.6, SDTLC=0.7, power=80%,

=0.05) ......................................................................................................................... 20

3.6 Hypotheses ............................................................................................................................. 20

4 DATA ANALYSIS CONSIDERATIONS ............................................................................... 21

4.1 Clinical Trial Datasets ........................................................................................................... 21

4.2 Determination of patient characteristics to be used to propensity score matching .............. 21

4.3 Assessment of suitability of final propensity score model for matching ............................... 22

4.4 Identify matches between BLISS LTE (treatment) patients and TLC (comparison) patients, using propensity score with a 2:1 match ratio (for US LTE data used for study) . 22

4.5 Assess degree of post-matching balance in predictors used in propensity score model across treatment and comparison groups ............................................................................ 23

4.6 Index date for patients in the TLC ........................................................................................ 23

4.7 Withdrawals from BLISS LTE trials ...................................................................................... 23

4.8 Analysis of propensity score matching variables ................................................................. 23

4.9 Rationale for inclusion of decade of study entry as a covariate ........................................... 23

9

4.10 Descriptive Statistics ............................................................................................................. 24

4.11 Statistical analysis of endpoints............................................................................................ 24

4.11.1 Primary endpoint ........................................................................................................... 24

4.11.2 Secondary endpoints .................................................................................................... 24

4.11.3 Exploratory endpoints ................................................................................................... 25

4.12 Diagnostics ........................................................................................................................... 26

4.12.1 Baseline characteristics of study arms ......................................................................... 26

4.12.2 Baseline characteristics of sample versus population .................................................. 26

4.12.3 Distribution of year 5 data point timing ......................................................................... 26

4.12.4 Patients withdrawing for LTE and TLC Cohorts ........................................................... 26

4.12.5 BLISS subjects that did not enroll in LTE ..................................................................... 26

4.12.6 BLISS LTE subjects randomized to SoC ...................................................................... 27

4.12.7 Belimumab baseline of BLISS LTE subjects ................................................................ 27

5 LIMITATIONS ....................................................................................................................... 27

6 STUDY CONDUCT, MANAGEMENT & ETHICS ................................................................ 28

6.1 Ethics Committee/IRB Approval ........................................................................................... 28

6.2 Informed Consent ................................................................................................................. 28

6.3 Data Protection ..................................................................................................................... 28

6.4 Personally Identifiable Information (PII) ............................................................................... 28

6.5 Adverse Event (AE), Pregnancy Exposure, and Incident Reporting .................................... 29

7 EXTERNAL INVOLVEMENT ............................................................................................... 29

7.1 Third Party Supplier .............................................................................................................. 29

7.2 External Expert/Health Care Professionals (Consultants & Research PIs) ......................... 29

8 Appendix 1.External Cohort Comparison Report: ................................................................ 32

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ABBREVIATIONS

LTE Long-term extension

PS Propensity score

SDI Systemic Lupus International Collaborating Clinics/American College of Rheumatology (ACR) Damage Index

SLE Systemic lupus erythematosus

SLEDAI Systemic Lupus Erythematosus Disease Activity Index

SoC Standard of care

TLC Toronto Lupus Cohort

Tx Treatment

11

1 INTRODUCTION/BACKGROUND

Two Phase 3 randomized controlled trials of intravenously (IV) administered belimumab have established the clinical effectiveness of belimumab plus standard of care (SoC) versus SoC alone at 52 (BLISS 52) and 76 (BLISS 76) weeks. The long term extensions (LTE) (BEL112233 and BEL112234) of these trials, however, did not have comparison SoC arms. Thus the question of the long-term relative efficacy of belimumab with SoC versus SoC alone remains unanswered. The purpose of this study is to provide a long-term comparative analysis between belimumab plus SoC versus SoC alone in the treatment of systemic lupus erythematosus (SLE). It plans to do so by comparing BLISS LTE patients to propensity score-matched SLE patients with similar baseline characteristics taken from an external SLE cohort. A systematic review of the literature was previously performed to identify research cohorts of SLE patients (attached as Appendix A).1 The review identified the Toronto Lupus Cohort (TLC) as the preferred source of SoC data for this study based on the size of the cohort, the extent of organ damage seen in the patients and severity of SLE disease activity. A subset of the TLC with patient baseline characteristics similar to the BLISS trials has previously been used in a GSK study of mortality and damage progression in SLE. The use of a similar subset of the TLC is envisioned in this study. This will be the first analysis of long term efficacy of belimumab plus SoC versus SoC alone. The primary analysis will take place on data from the US LTE trial (BEL112233) versus the TLC patient data. As an exploratory sensitivity analysis the same analysis will be performed on the pooled BLISS LTE dataset to be constructed from the two BLISS LTE trials (BEL112233 and BEL112234) versus the TLC. Throughout the remainder of this document “belimumab treatment” refers to treatment with belimumab supplemented by SoC while SoC refers to SoC alone. Similarly, “TLC” throughout the remainder of this document refers to a subset of the TLC with patient characteristics similar to the patient baseline characteristics in the BLISS trials. Observations in both the LTE and TLC data may not fall on annual intervals, thus, for instance, a 5th year observation will be the first observation to take place ≥ 5 years but before 6 years from the index date.

2 OBJECTIVES

2.1 Primary To compare the mean change in SDI scores from baseline to year 5 between patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC.

2.2 Secondary To compare the time to first SDI worsening between patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC.

12

To compare the total SDI score at yearly intervals between patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC. To perform MSM transition analyses of SDI independently for the belimumab and SoC groups using the Jackson et al. 2011 methodology based on data from the US BLISS LTE trial (BEL112233) and the TLC.2,3,4 To describe the change from baseline in SDI organ damage system (ocular, neuropsychiatric, renal, pulmonary, cardiovascular, peripheral vascular, gastrointestinal [GI], musculoskeletal, skin, premature gonadal failure, diabetes and malignancy) summarized by year interval of patients treated with belimumab or SoC, based on data from the US BLISS LTE trial (BEL112233) and the TLC.

2.3 Exploratory As a sensitivity analysis, the primary and secondary objectives above will be retested using the pooled BLISS LTE dataset to be constructed from the BLISS LTE trials (BEL112233 and BEL112234) and the TLC.

3 RESEARCH METHODOLOGY

3.1 Study Design This is a longitudinal propensity score-matched study comparing individual patients of the BLISS LTE trial(s) to clinically and demographically similar patients in the TLC.

3.2 Study Population

3.2.1 Eligibility Criteria

3.2.1.1 Inclusion Criteria

• Diagnosis of systemic lupus erythematosus (ICD-9 710.0) using ≥ 4 of 11 American College of Rheumatology (ACR) criteria (710.0)

• ≥ 18 years of age

• SELENA SLEDAI/SLEDAI-2K score ≥ 6 at baseline

• Auto-antibody positive (anti-nuclear antibody ≥ 1:80 and/or anti-dsDNA ≥ 30 IU/mL)

3.2.1.2 Exclusion Criteria

• Active severe lupus nephritis or central nervous system lupus

• Receipt of B cell target therapy at any time

• For TLC patients, previous use of belimumab

13

3.2.2 Sampling

All patients in the BLISS LTE trial(s) will be propensity score-matched to patients in the TLC meeting the inclusion and exclusion criteria above. The numbers of patients available from each source appear in the table below.

Sample Sizes Study start 1 year 2 year 5 year

TLC with SLEDAI ≥ 6 5 649 649 649 536

Study start Year 1-2 Year 2-3 Year 5-6

US BLISS LTE (BEL112233)6 268 259 244 192

LTE pooled dataset MITT7 998 955 861 531

3.2.3 Matching Procedure

For purposes of propensity score matching, the baseline for BLISS LTE subjects is defined as the date of first exposure to belimumab in the BLISS trials. The figure below illustrates the definition of the belimumab baseline for subjects enrolled in the US BLISS LTE (BEL112233).

• For subjects randomized to belimumab 10 mg/kg in BLISS 76 (BEL110751), the belimumab baseline is the core baseline (baseline at randomization) in BLISS 76 (BEL110751).

• For subjects randomized to belimumab 1 mg/kg in BLISS 76 (BEL110751), the belimumab baseline is the core baseline (baseline at randomization) in BLISS 76 (BEL110751).

• For subjects randomized to placebo in BLISS 76 (BEL110751), the belimumab baseline is the extension baseline (baseline at start of LTE) in US BLISS LTE (BEL112233).

Definition of belimumab baseline for BLISS US LTE (BEL112233) subjects

The matching procedure is illustrated in the following figure. Collectively, the subjects enrolled in the BLISS US LTE (BEL112233) comprise the belimumab cohort of the primary analysis. Propensity score matching is performed by comparing the characteristics of BLISS subjects at their respective belimumab baselines with the characteristics of TLC patients at their respective index dates. Data analysis considerations for defining the index dates for TLC patients are discussed in section 4.6.

Core

Baseline

Extension

Baseline

Belimumab Baseline

BLISS 76 Open label extension (BEL112233)

PBO BEL 10mg ≤5 years of follow-upPlacebo

BEL 1mg BEL 10mg ≤6.5 years of follow-upBEL 1mg/kg

BEL 10mg ≤6.5 years of follow-upBEL 10mg/kg

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Propensity score matching procedure for primary analyses

The procedure outlined above ensures that the propensity score matching is performed against the point in time at which patients in the different cohorts were assigned to different treatments. However, it introduces two potential sources of biases:

• For BLISS LTE subjects randomized to belimumab 1 mg/kg or belimumab 10 mg/kg in the parent study, the follow-up period includes the duration of the parent study. However, for BLISS subjects randomized to placebo in the core study and for the TLC cohort, the follow-up period does not contain any period of enrollment in a randomized clinical trial. Differences in quality of care obtained within the setting of the randomized clinical trial, versus the quality of care during the LTE or in the TLC, may lead to differences in outcomes during the follow-up period for BLISS LTE subjects randomized to belimumab in the parent study versus BLISS LTE subjects randomized to placebo in the parent study and the TLC cohort.

• For BLISS LTE subjects randomized to belimumab 1 mg/kg in the parent study, the follow-up period includes the period of exposure to belimumab 1 mg/kg. However, for BLISS subjects randomized to belimumab 10 mg/kg or placebo in the parent study, the subjects were exposed to belimumab 10 mg/kg for the entire follow-up period. A dose response effect may lead to differences in outcomes for BLISS LTE subjects randomized to belimumab 1 mg/kg in the parent study versus BLISS LTE subjects randomized to belimumab 10 mg/kg or placebo in the parent study.

Diagnostics to test for these potential effects are discussed in sections 4.12.6 and 4.12.7.

BenlystaBLISS

TLC SoC

Belimumab

Baseline

PBO BEL 10mgPlacebo

BEL 1mg BEL 10mgBEL 1mg/kg

BEL 10mgBEL 10mg/kg




PS matching

conducted at

belimumab Baseline

Collectively, these three groups make up the ‘BLISS arm’ of the comparison

Up to 6.5 years of exposure

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3.2.4 Matching Criteria

The set of criteria to be considered for inclusion in the propensity score model will be based on patient level characteristic predictors of organ damage. Potential criteria were identified by reviewing the literature for factors predicting organ damage. Only baseline patient characteristics available in both the BLISS LTE data and the TLC data will be evaluated for inclusion (see 3.2.3). Final predictors of organ damage to be used in the propensity score model will be determined using statistical evaluation criteria (see 3.2.4.2) subject to validation by two experts in the treatment and research of SLE. Matching criteria will be restricted to baseline characteristics because post-baseline variables could be confounded by treatment effects. As discussed in section 3.2.3, the matching criteria for BLISS LTE subjects are based on the first date of exposure to belimumab.

• Randomized to belimumab 10 mg/kg in BLISS 76: Matching criteria are based on characteristics at BLISS 76 baseline.

• Randomized to belimumab 1 mg/kg in BLISS 76: Matching criteria are based on characteristics at BLISS 76 baseline.

• Randomized to SoC in BLISS 76: Matching criteria are based on characteristics at first exposure to belimumab in the BLISS LTE (BEL112233) (extension baseline).

• If the data recorded for characteristics at extension baseline are identical to the characteristics recorded at BLISS 76 baseline, then the data will be used as-is.

• If there is no distinct data field available for a characteristic at extension baseline, then the value of the characteristic at BLISS 76 baseline will be used as a proxy.

3.2.4.1 Predictors of Organ Damage Progression Reported in the Literature, Availability in the TLC and BLISS LTE Data, and Candidate Variables to be used or Propensity Score Adjustment

Predictor TLC BLISS LTE

(US / Pooled) Potential PS

matching factor

Age5,8,9 Yes Yes / Yes Yes

Gender8,9 Yes Yes / Yes Yes

Race, Ethnicity8,9 Yes Yes / Yes Yes

Household income8 SES data No / No

Geocode proxy?

Educational attainment8 SES data No / No

Geocode proxy?

Disease duration8,9 Yes Yes / Yes Yes

Current Smoker5 Yes Probably / Probably

Probably

History-hypertension8 Yes§ Yes / Yes Yes

History-dyslipidemia Yes‡‡ Proxy†† No

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History-proteinuria8 Yes‡‡ Yes / Yes Yes

History-lupus anticoag positivity8

Yes‡‡ No / No No

Baseline # ACR criteria satisfied8

At entry† No§§ / No§§ No

Baseline SLEDAI score5 Yes Yes / Yes‡ Yes

Disease activity over time (i.e., time-weighted SLEDAI)9,10,11 Yes Yes / No

No (not a baseline variable)

Corticosteroid use/dose5,8,9 Yes§ Yes / Yes†† Yes

Hydroxychloroquine/other antimalarial drug use5,8,9*

Yes§ Yes / Yes†† Yes

Cyclophosphamide/other immunosuppressive use5,8,9**

Yes§ Yes / Yes†† Yes

Baseline SDI9,12,13 Yes Yes / Yes Yes *TLC uses “antimalarial” drug variable but Petri et al. (2012) uses ‘hydroxychloroquine’ (specific antimalarial).

**TLC uses “immunosuppressive” drug variable but Petri et al. (2012) uses ‘cyclophosphamide’ (specific immunosuppressive).

† The SLE Diagnostic Check List is completed on entry to the cohort.

‡ SLEDAI score at baseline and at last visit of parent trial (BLISS 52/76) should be available.

†† Concomitant medications are comprehensively reported in the BEL112233 clinical study report. Statin use is a potential proxy for presence of dyslipidemia; however, it is inexact due to the use of statins for other medical conditions.

§ Every visit ‡‡ Annual test §§ Email from February 3, 2016.

3.2.4.2 Approach to be used to select predictors of organ damage to be included in final propensity score model

• The specific functional form used for the propensity score model does not have a profound impact on propensity score model performance, but following convention a logistic regression approach will be used.

• The model specification initially will include as independent variables all plausible (and available) predictors as listed in the table in section 3.2.4.1 as well as predictors recommended by external experts. In some cases, proxy measures may be used for unavailable predictors (e.g., geocode-level measures of population income or educational attainment)

• Drop the least statistically significant predictor from inclusion in propensity score model, then drop the next least statistically significant predictor, etc. until all included predictors have a p-value <0.1 (backward elimination)

• The specific predictors of organ damage to be included as covariates in the final model will be based on the model specification with the minimum Akaike information criterion (AIC) value, subject to modification as needed based on recommendations of external experts.

• Particular attention will be devoted to assessing the adequacy of the match for baseline SDI score (as likely the most important predictor of future organ damage), by comparing

PPD

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the frequency distribution of baseline SDI scores for the treatment and comparison samples.

3.3 Data Source / Data Collection

3.3.1 TLC Data

The TLC is composed of patients seen at the who have agreed to participate in the cohort. Participants agree to a protocol that includes visits every 2 to 6 months regardless of disease activity.14 To insure standardization of assessments, training sessions are held for each new set of clinic providers.14

3.3.1.1 Instruments Completed on Entry to Cohort

The SLE Diagnostic Check List and 1000 Faces Cultural Background instruments are completed at entry to the cohort.15

3.3.1.2 Data Collected at Every Visit

The Lupus Protocol Version 6.0.1, a 560 question instrument, is completed at each visit. Questions are included on demographics, vitals, infections, cancer, lifestyle, organ systems (head and neck, retina, mucous membranes, respiratory, cardiac, vascular, gastrointestinal, reticuloendothelial, renal, fertility, skin, muculoskeletal, neurophychiatric, and endocrine), and therapies (NSAIDS, steroids, antimalarials, immunosuppressives, biologics, anticoagulants and antiplatelet).16 At each visit comprehensive sets of standard and lupus-related blood work are performed.

3.3.1.3 Data Collected Annually

Annually the SLICC Damage Index, SF36, Fatigue Severity Scale, and Family History instruments are completed. 15 Additional lupus-related bloodwork is performed on an annual basis.

3.3.1.4 Synchronization of Observations between TLC and LTE data

Per protocol, visits in the TLC are every 3 to 6 months with SDI, SF36, fatigue and family history collected on an annual basis. The timing of visits in the LTE trials varied between the parent study and extension phase. As shown in the figures of section 3.2.3, the baseline of the LTE study varies by treatment in the parent study. The baseline for patients receiving belimumab is the baseline of the parent study, while the baseline for patients receiving SoC is the baseline of the extension phase. The frequency of assessment in the parent study was generally every 4 weeks, except for SDI, which was assessed at 52 weeks and last visit. In the extension phase it was every 24 weeks, except for SDI and QoL, which were assessed every 48 weeks.

PPD

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Data points will be synchronized at 6-month and annual intervals using a conservative algorithm to adjust for differences in assessment frequency. We recognize that bias can be introduced into time-to-event analyses by the frequency of the observations.

3.4 Endpoints

3.4.1 Primary Endpoint

The difference in change in SDI from baseline to year 5 interval between patients treated with belimumab or SoC, based on the data of the US BLISS LTE trial (BEL112233) and the TLC.

3.4.2 Secondary Endpoint(s)

The analysis of all secondary endpoints will use data from the US BLISS LTE trial (BEL112233) for patients treated with belimumab and data from the TLC for patients treated with SoC. The difference in time to first SDI worsening between patients treated with belimumab or SoC. The change from baseline SDI score by year interval for patients treated with belimumab or SoC. The difference in change from baseline SDI score by year interval between patients treated with belimumab or SoC. Transition analysis of SDI from baseline over a 5-year period for patients treated with belimumab or SoC. Change from baseline in SDI organ damage system (ocular, neuropsychiatric, renal, pulmonary, cardiovascular, peripheral vascular, gastrointestinal [GI], musculoskeletal, skin, premature gonadal failure, diabetes and malignancy) summarized by year interval for patients treated with belimumab or SoC. The frequency of increase from baseline in SDI organ damage system subscores between patients treated with belimumab or SoC. The difference in mean SLEDAI score from baseline over a 5-year period. The difference in cumulative corticosteroid usage from baseline over a 5-year period.

3.4.3 Exploratory Endpoint(s)

The analysis of all exploratory endpoints will use the pooled BLISS LTE dataset to be constructed from the BLISS LTE trials (BEL112233 and BEL112234) for patients treated with belimumab and data from the TLC for patients treated with SoC.

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The difference in change in SDI from baseline to year 5 interval between patients treated with belimumab or SoC The difference in time to first SDI worsening between patients treated with belimumab or SoC. The change from baseline SDI score by year interval for patients treated with belimumab or SoC. The difference in change from baseline SDI score by year interval between patients treated with belimumab or SoC. Transition analysis of SDI from baseline over a 5 year period for patients treated with belimumab or SoC. Change from baseline in SDI organ damage system (ocular, neuropsychiatric, renal, pulmonary, cardiovascular, peripheral vascular, gastrointestinal [GI], musculoskeletal, skin, premature gonadal failure, diabetes and malignancy) summarized by year interval for patients treated with belimumab or SoC. The difference in cumulative corticosteroid usage from baseline over a 5-year period.

3.5 Sample Size / Power Calculations

3.5.1 Available sample sizes for BLISS LTE and TLC data after propensity score matching

Belimumab TLC Comparison

US LTE Sample (≥ 5 years Tx duration) with 2:1 Match

192 384

Pooled LTE Sample (≥5 years Tx duration) with 1:1 Match*

530 530

Tx = treatment. *TLC sample size is not sufficient for 2:1 match

• As reported in the TLC mortality and damage progression study, the maximum number of TLC patients available for matching who meet inclusion criteria and who have at least 5 years of post-index date data is 536, which is not sufficient for 2:1 matching for the pooled LTE sample.

• For the comparative effectiveness analysis of the US LTE sample only, 2:1 matching may be possible, but at most 192 belimumab patients and 384 propensity score-matched TLC patients would be available for use in the comparative effectiveness for the primary study endpoint.

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3.5.2 Sample size requirements for “Mean Change from Baseline SDI” endpoint: Alternative

treatment effect size assumptions (SDLTE=0.6, SDTLC=0.7, power=80%, =0.05)

SAMPLE SIZE Change from Baseline SDI

Difference

Belimumab Comparison Belimumab Comparison Units %

2669 2669 0.3 0.35 0.05 14%

1187 1187 0.3 0.375 0.08 20%

668 668 0.3 0.40 0.10 25%

552 552 0.3 0.41 0.11 27%

530 530 0.3 0.413 0.113 27%

395 395 0.3 0.43 0.13 30%

297 297 0.3 0.45 0.15 33%

212 424 0.3 0.45 0.15 33%

200 400 0.3 0.455 0.155 34%

156 312 0.3 0.475 0.18 37%

• Using the US-only LTE data for analysis of the 5-year Mean Change from Baseline SDI, the available sample size would be at most 192 belimumab patients and 384 propensity score-matched TLC patients (see 3.5.1), which would provide 80% power to detect a treatment effect size of a 0.155 unit difference (or 34% change) for a two-tailed test. [For a one-tailed test, a treatment effect size of a 0.138 unit difference (or 32%) could be detected.]

• Using the pooled LTE data for analysis of the 5-year Mean Change from Baseline SDI, under a “best case scenario” the available sample size would be 530 belimumab patients and 530 propensity score-matched TLC patients (see 3.5.1), which would provide 80% power to detect a treatment effect size of a 0.113 unit difference (or 27% change) for a two-tailed test. [For a one-tailed test, a treatment effect size of a 0.099 unit difference (or 25% change) could be detected.]

• However, given that there are only 536 potential comparison matches in the TLC data (see 3.2.2), it is likely that a suitable match for some of the 531 pooled LTE patients will not be found, especially given differences in patient demographics (e.g., race) across the geographies represented in the pooled LTE sample. If, for example, 395 of the pooled LTE sample (approximately 75%) are matched to 395 TLC patients, a treatment effect size of a 0.13 unit difference (or 30% change) could be detected with 80% power (two-tailed test). If only 297 of the pooled LTE sample (approximately 60%) can be matched, a treatment effect size of a 0.15 unit difference (33% change) could be detected with 80% power.

• Power for the pooled analysis will also be diminished to some degree by the need to include some form of statistical adjustment for differences in health system characteristics across the countries represented in the pooled LTE sample.

3.6 Hypotheses

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• The change in SDI from baseline to year 5 will be the same for patients receiving belimumab as for patients receiving SoC.

• The time to first SDI worsening will be the same for patients receiving belimumab as for patients receiving SoC.

• The total SDI score at each year interval will be the same for patients receiving belimumab as for patients receiving SoC.

• The frequency of increase in SDI organ damage subscores will be the same for patients receiving belimumab as for patients receiving SoC.

4 DATA ANALYSIS CONSIDERATIONS

Subgroup analysis is not planned due to the limited power to detect statistical significance within subgroups.

4.1 Clinical Trial Datasets Patient characteristics for the belimumab patients will be extracted from the following analysis datasets from the BLISS LTEs (BEL112233 and BEL112234):

• ADSL: Subject Level analysis dataset

• ADCOV: Subject Level Summary Analysis

• ADMH: Medical History Analysis

• ADCM: Concomitant Medications Analysis

• ADVS: Vital Sign Analysis

• ADSLICC: SLICC/ACR Damage Index Analysis

• ADSV: Subject Visit Analysis

• ADSLEDAI: SELENA SLEDAI Analysis

• ADCMYR: SLE Medications Analysis • ADSFI: SLE Flare Index Analysis

4.2 Determination of patient characteristics to be used to propensity score matching

• Patient characteristics shown to be potentially predictive of organ damage in the literature that are available in both the BLISS LTE and TLC data will considered for inclusion in the propensity score logistic regression model to be used for matching (see Table under 3.2.4.1)

• An initial model including all candidate predictors will be modified by sequentially dropping the least statistically significant predictor from inclusion in propensity score model, until all included predictors have a p-value <0.1 (backward elimination)

• The final model to be used for propensity score matching will be based on the model variant with the minimum Akaike information criterion (AIC) value.

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• Final predictors of organ damage will be subject to validation by two experts in the treatment and research of SLE.

4.3 Assessment of suitability of final propensity score model for matching

• Determine range of common support for the logit index values (i.e., “X”) across belimumab and SoC patients

o If range of common support is too narrow, the propensity score model will be re-evaluated to determine if an alternative specification would improve common support

o Recommend refraining from pre-trimming patients off of the common support, given planned use of caliper matching, to preserve sample size.

• Examine propensity score model statistics (e.g., Hosmer–Lemeshow statistic, c-statistic, McFadden’s R2) to assure model has adequate predictive qualities.

4.4 Identify matches between BLISS LTE (treatment) patients and TLC (comparison) patients, using propensity score with a 2:1 match ratio (for US LTE data used for study)

• Matching will be based on the propensity score defined as the untransformed logit

index value (e.g., “X” for each individual from the logistic regression sample), rather than predicted probability, to increase variance at extremes of the propensity score distribution.

• The initial matching approach is to select for each treatment patient the “nearest neighbor” match among patients in the TLC comparison sample.

Matching for each treatment patient from the pool of TLC comparison patients proceeds (without replacement) until all treatment patients are matched

The process is then repeated to obtain the second comparison match for each treatment patient.

• The matching effort initially will employ a “rule of thumb” caliper (equal to 25% of the

standard deviation of the X distribution from the propensity score model) to try to eliminate the potential for excessively dissimilar “nearest” matches. As a result, some treatment patients may be dropped from the sample due to lack of adequate match.

• To focus on preserving sample size, the matching effort will be repeated with the caliper relaxed as needed to assure matches are attained for all treatment patients. This will likely decrease the extent of improvement in the balance for predictor variables resulting from propensity score matching. However, the caliper will not be relaxed to an extent that results in unacceptable post-match balance (see 4.5).

• Also will consider using inverse propensity score weighting approach as an alternative to matching to preserve sample size if needed (e.g., if achieving adequate balance via propensity score matching reduces the usable sample size to a degree that would jeopardize study power).

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4.5 Assess degree of post-matching balance in predictors used in propensity score model across treatment and comparison groups

• The post-match “standardized bias” (SB) for each predictor included in the propensity

score model, calculated as

SB = 100 [(X̅T X̅C) / (0.5 {Var(XT) + Var(XC)}½], will be examined to assure that SB < 5% for all or most of the included predictors of organ damage.

• The trade-off between greater bias reduction (lower values of SB), associated with

fewer but more exact matches for treatment patients to comparison patients, versus

retention of sample size (associated with larger caliper value for matching) will be

assessed with the matching approach modified as needed.

• Given limited sample size, trade-off assessments will emphasize sample preservation

over precision in post-match balance, while maintaining adequate balance (e.g., the

SB for predictors of organ damage included in the final propensity score model must

be < 5% for all or most of the included predictors).

4.6 Index date for patients in the TLC

The index date for TLC patients will be the first point in their clinical record that their SLEDAI score reaches the SLEDAI inclusion criteria of the BLISS trials ( ≥ 6.0). An analysis will be made of the variability of SLEDAI scores in TLC and LTE patients to determine whether a single SLEDAI score ≥ 6 is sufficient to set the index date or that a sustained period ≥ 6 should be required. This analysis as well as clinical input will guide the final establishment of the index date criteria for TLC patients.

4.7 Withdrawals from BLISS LTE trials The population for time to event endpoints will include the MITT population of the BLISS LTE trial(s). The population for other endpoints will include only the populations available for analysis at that time point i.e., no imputation will be done to include patients who have withdrawn from the trial(s) by that point.

4.8 Analysis of propensity score matching variables

A subset of variables enumerated in 3.2.4.1 will be used to match patients from the BLISS LTE trial(s) to those in the TLC as described above. After matching has taken place, a comparison of each matching variable for belimumab patients to SoC patients will be performed. If a substantial difference is detected between treatment groups (belimumab and SoC) that variable will be added as a covariate to the statistical analysis of endpoints described below.

4.9 Rationale for inclusion of decade of study entry as a covariate

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The decade of entry into the TLC is specified as a covariate in 4.11.1, 4.11.2.1, 4.11.2.3, 4.11.2.6, 4.11.2.7 and 4.11.2.8 below. The reason for doing so is that while the treatment of SLE has not changed greatly over the past decades and treatment in Canada and the US are comparable, the use of corticosteroids has abated in recent years as their role as a risk factor for organ damage has become more apparent. While it would be desirable to use the decade of entry as a propensity score matching variable, all LTE patients would be in a single decade, severely limiting the LTC patients that could be matched.

4.10 Descriptive Statistics Descriptive statistics will be performed on baseline demographic and clinical characteristics for both the belimumab-treated and SoC arms. This analysis will include a comparison of the two arms with p values.

4.11 Statistical analysis of endpoints All inferential statistics will be two-tail tests performed with an alpha of p=.05.

4.11.1 Primary endpoint

Change of SDI from baseline to censoring will be evaluated using linear regression with change of SDI from baseline as the dependent variable, and with a variable indicating treatment group (belimumab or SoC) and matching variable(s) determined in 4.8 above as covariates. The decade of entry into the study will also be a covariate.

4.11.2 Secondary endpoints

4.11.2.1 Difference in time to first SDI worsening

Cox proportional hazards regression will be used to estimate the hazard ratio between belimumab and SoC and its statistical significance. If substantial differences are found among matching variables in 4.8 above, they will be added as covariate(s). The decade of entry into the study will also be a covariate.

4.11.2.2 Change from baseline SDI score by year interval

Descriptive statistics of the change from baseline SDI score will be estimated at the end of years 1 through 5 for both the belimumab and SoC groups.

4.11.2.3 Difference of change from baseline SDI by year interval

Change of SDI from baseline to end of years 1 through 5 will be evaluated using linear regression with change of SDI from baseline as the dependent variable, and with a variable indicating treatment group (belimumab or SoC) and matching variable(s) determined in 4.8 above as covariates. The decade of entry into the study will also be a covariate.

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4.11.2.4 Transition analysis of SDI

Multi-state Markov modelling transition analysis of SDI will be performed independently for the belimumab and SoC groups using the Jackson et al. 2011 methodology.2,3,4 This methodology calculates transition probabilities between health states over time, in this case health states defined by SDI strata.

4.11.2.5 Change from baseline of SDI organ damage system subscores

Descriptive statistics of the change from baseline SDI organ system subscores will be estimated at the end of years 1 through 5 for the belimumab and SoC groups.

4.11.2.6 Frequency of increase from baseline of SDI organ damage system subscores

Frequency of increase of SDI organ system subscores from baseline to censoring between patients treated with belimumab or SoC will be evaluated using logistic regression with a variable indicating treatment group (belimumab or SoC) as the dependent variable, and with the change of SDI organ system subscore from baseline and matching variables determined in 4.8 above as covariates. The decade of entry into the study will also be a covariate.

4.11.2.7 Mean SLEDAI score

Mean SLEDAI score from baseline through year 5 will be evaluated using linear regression with mean SLEDAI score as the dependent variable and with a variable indicating treatment group (belimumab or SoC) and matching variable(s) determined in 4.8 above as covariates. The decade of entry into the study will also be a covariate.

4.11.2.8 Cumulative corticosteroid usage

Cumulative use of corticosteroids from baseline through year 5 will be evaluated using linear regression with cumulative corticosteroid use as the dependent variable and with a variable indicating treatment group (belimumab or SoC) and matching variable(s) determined in 4.8 above as covariates.

4.11.3 Exploratory endpoints

The methods described above will also be used for the corresponding exploratory endpoints. The following exploratory analyses will be conducted separately for the LTE and TLC cohorts: study completers will be compared to non-completers for a) baseline subject characteristics and b) SDI scores by follow-up year (ie., SDI in year 1 for the subjects that drop out in year one, subjects that complete year 1 but drop out later and subjects that complete the full 5 years; the same comparison for year 2 and so on). The following exploratory analyses will be conducted separately for the LTE and TLC cohorts and for the two cohorts combined: a time-to-event analysis, such a Cox proportional hazards model, will be employed to estimate the relative risk of a series of endpoints: time to SDI

26

increase, varying from 1 point to 3 points, as well as time to a specific absolute SDI score (scores to be determined), with baseline SDI being a key covariate.

4.12 Diagnostics

4.12.1 Baseline characteristics of study arms

Study arms will be tested for statistically significant differences in patient baseline characteristics using t tests and Fisher’s exact tests, as appropriate.

4.12.2 Baseline characteristics of sample versus population

Matched samples will be tested for statistically significant differences in patient baseline characteristics using t tests and Fisher’s exact tests, as appropriate.


Patients in the TLC are not seen at specific intervals. Likewise, patients originating in belimumab and SoC arms of the underlying belimumab trials have different intervals of observation. Therefore the 5th year observation in both arms will take place at time points not strictly 5 years from baseline. The distributions of time from baseline of the 5th year observation will be reported.

4.12.4 Patients withdrawing for LTE and TLC Cohorts

Analysis will be conducted that includes all study participants in both cohorts, i.e. subjects that complete the full five years of follow-up as well as subjects that drop out before study end. The impact of the dropout rates will be assessed by comparing those who completed the study versus those who did not complete the study in terms of baseline and clinical characteristics. Time to event analyses will be used to test for differences in clinical outcomes including SRI response, flares, and increase in SLICC Damage Index.

4.12.5 BLISS subjects that did not enroll in LTE

Analysis will be conducted to detect selection bias among the BLISS subjects who enrolled in an LTE study versus BLISS subjects who did not proceed to enroll in an LTE. This analysis will compare LTE subjects with subjects in the corresponding parent study who either discontinued the parent study before completion, or completed the parent study but did not enroll in the LTE. The potential presence of selection bias will be assessed in terms of baseline and clinical characteristics, and time to event analyses will be used to test for differences in clinical outcomes during the parent study.

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4.12.6 BLISS LTE subjects randomized to SoC

Analysis will be conducted to test for study effects associated with differences in quality of care obtained within the setting of the randomized clinical trials (see section 3.2.3). These analyses will be performed by comparing BLISS subjects randomized to SoC against TLC patients to determine whether enrollment in the randomized clinical trial had a significant effect on clinical outcomes associated with SoC. Propensity score matching will be used to match BLISS SoC subjects to TLC patients based on the BLISS subjects’ characteristics at core baseline, e.g., at randomization to SoC + placebo in BLISS 76 (BEL110751). Time to event analyses will be used to test for differences in clinical outcomes during the parent study compared with clinical outcomes in the TLC over a follow-up period equal to the duration of the parent study. This analysis will be conducted as early in the process as possible and assessed for statistical significance. If it is statistically significant, it will be assessed for clinical significance. If both statistically and clinically significant, methods will be adopted to mitigate this bias in further analyses , e.g., by incorporating fixed effects or stratification into the statistical models, or by performing subgroup analyses of BLISS LTE subjects by randomized study medication in the parent study.

4.12.7 Belimumab baseline of BLISS LTE subjects

Analysis will be conducted to test for potential biases introduced by the method used to define belimumab baseline for the propensity score matching procedure (section 3.2.3). This analysis will focus on tests of equivalence among the subgroups of BLISS LTE subjects randomized to belimumab 10 mg/kg in the parent study, BLISS LTE subjects randomized to belimumab 1 mg/kg in the parent study, and BLISS LTE subjects randomized to placebo in the parent study. Time to event analyses will be used to test for differences in clinical outcomes during the follow-up period of the comparative effectiveness analysis.

5 LIMITATIONS

The primary limitations are the numbers of patients in the BLISS LTE trials, the number of patients in the TLC, and the number of patients with matching characteristics. This limits the power to reach statistically significant conclusions. Other limitations include:

• Dissimilar data collection cycles, i.e. data in the BLISS LTEs were collected on a pre-defined schedule, while data in the TLC are collected when patients schedule visits.

• The controls will not have been randomly selected from the same population as the BLISS LTE patients. Instead propensity score matching will be used to match belimumab patients with controls based on a number of patient characteristics.

• BLISS patients may have received care from different types of health care systems than external cohort controls.

• SELENA-SLEDAI scores are available for BLISS LTE patients while SLEDAI-2K scores are available for TLC patients. While the components of SELENA-SLEDAI remain the same as the SLEDAI-2K, the definitions of several components are slightly different.17

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The length of assessment was also different in the BLISS trials than in the TLC; up to 10 days prior to a visit in the BLISS trials while up to 30 days prior to a visit in the TLC.18,19 A study has indicated that there is minimal difference between 10 and 30 days assessment.20 The SELENA-SLEDAI is yet to be rigorously validated.21

• Patients were free to withdraw from the BLISS studies and from the TLC. This may introduce bias.

• The sensitivity analysis will utilize pooled patient-level data from both BLISS LTE trials (BEL112233 and BEL112234), treating the data as through from one, rather than two, trials. In fact the populations and health systems through which care was given may have been significantly different. The preferred method of aggregating data from multiple trials is meta-analysis. However, using meta-analysis for just two trials would result in loss of power. The pooled dataset will be constructed de novo from the datasets of the BLISS LTE trials (BEL112233 and BEL112234) as part of this analysis.

• The BLISS trials used 48 weeks as a year.

• The PS matching approach to be used only accounts for sample selection bias (aka confounding by indication) for the observed confounders (predictors of organ damage also potentially affecting treatment assignment) included in the PS model. To the extent additional clinically important confounders exist in the data but cannot be observed, sample selection bias cannot be fully addressed using PS adjustment methods. However, it is likely that in this study that differential access to belimumab across the TLC and LTE cohorts is likely to be the primary factor driving differences in treatment received for clinically similar patients, not unobserved confounders.

• The TLC is treated at a single Canadian clinical site while the BLISS BEL112233 LTE trial was conducted at multiple US sites. Differences in outcomes may be confounded by differences in the national health systems. Outcomes may also be confounded by treatment practices specific to the TLC clinical site.

6 STUDY CONDUCT, MANAGEMENT & ETHICS

6.1 Ethics Committee/IRB Approval IRB approval will be sought from the University of Toronto IRB.

6.2 Informed Consent Consents have previously been obtained for use of the data. No new data will be collected.

6.3 Data Protection Data have been de-identified according to HIPAA standards.

6.4 Personally Identifiable Information (PII) Data have been de-identified according to HIPAA standards.

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6.5 Adverse Event (AE), Pregnancy Exposure, and Incident Reporting

Not applicable.

7 EXTERNAL INVOLVEMENT

7.1 Third Party Supplier Data will be analyzed by: Medical Decision Modeling Inc. 201 N. Illinois St., Ste. 1730 Indianapolis IN 46204

Data will be provided by:

Dr.

7.2 External Expert/Health Care Professionals (Consultants & Research PIs)

Dr. Consultant Dr. Consultant

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REFERENCES 1. Medical Decision Modeling Inc. Research of Lupus Cohorts as Comparator Arm in

Belimumab Subcutaneous Cost Effectiveness Model - Internal Document. January 2016.

2. Bruce IN, O’Keeffe AG, Farewell V, et al. Factors associated with damage accrual in patients with systemic lupus erythematosus: results from the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort. Ann Rheum Dis. 2015;74(9):1706-1713. doi:10.1136/annrheumdis-2013-205171.

3. Jackson C. Multi-state models for panel data: the msm package for R. J Stat Softw. 2011;38(8). https://www.jstatsoft.org/article/view/v038i08. Accessed February 24, 2016.

4. Jackson C. Multi-state modelling with R: the msm package. November 2015. https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf. Accessed February 24, 2016.

5. Impact of disease activity on mortality and damage progression in SLE patients with active disease despite standard of care using data from the Toronto Lupus Cohort - Preliminary Report. September 2014.

6. et al. A Multi-Center, Continuation Trial of Belimumab (HGS1006, LymphoStat-B), a Fully Human Monoclonal Anti-BLyS Antibody, in Subjects with Systemic Lupus Erythematosus (SLE) Who Completed the Phase 3 Protocol HGS1006-C1056 in the United States. December 2015.

7. Results Report for 201223, a Pooled Analysis of BEL112233 and BEL112234, Two Long-term, Extension Studies of BEL110751 and BEL110752, to Investigate Long-term Safety and Organ Damage Accrual with Belimumab Treatment in Systemic Lupus Erythematosus - GSK Internal Report. November 2015.

8. Petri M, Purvey S, Fang H, Magder LS. Predictors of organ damage in systemic lupus erythematosus: the Hopkins Lupus Cohort. Arthritis Rheum. 2012;64(12):4021-4028. doi:10.1002/art.34672.

9. Sutton EJ, Davidson JE, Bruce IN. The systemic lupus international collaborating clinics (SLICC) damage index: a systematic literature review. Semin Arthritis Rheum. 2013;43(3):352-361. doi:10.1016/j.semarthrit.2013.05.003.

10. Becker-Merok A, Nossent HC. Damage accumulation in systemic lupus erythematosus and its relation to disease activity and mortality. J Rheumatol. 2006;33(8):1570-1577.

11. Stoll T, Sutcliffe N, Mach J, Klaghofer R, Isenberg DA. Analysis of the relationship between disease activity and damage in patients with systemic lupus erythematosus--a 5-yr prospective study. Rheumatol Oxf Engl. 2004;43(8):1039-1044. doi:10.1093/rheumatology/keh238.

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12. Cardoso CRL, Signorelli FV, Papi J a. S, Salles GF. Initial and accrued damage as predictors of mortality in Brazilian patients with systemic lupus erythematosus: a cohort study. Lupus. 2008;17(11):1042-1048. doi:10.1177/0961203308093829.

13. Alarcón GS, Friedman AW, Straaton KV, et al. Systemic lupus erythematosus in three ethnic groups: III. A comparison of characteristics early in the natural history of the LUMINA cohort. LUpus in MInority populations: NAture vs. Nurture. Lupus. 1999;8(3):197-209.

14. Urowitz MB, Gladman DD. Contributions of observational cohort studies in systemic lupus erythematosus: the University of Toronto Lupus Clinic experience. Rheum Dis Clin North Am. 2005;31(2):211-221, v. doi:10.1016/j.rdc.2005.01.008.

15. Toronto Lupus Cohort. Lupus Database Research Program: Data collection forms.

16. The Lupus Clinic, Centre for Prognosis Studies in the Rheumatic Diseases. Lupus Protocol Version 6.0.1. 2012.

17. Touma Z. Derivation of an Appropriate Outcome Measure in Lupus. 2012. https://www.researchgate.net/publication/256443973_Derivation_of_an_Appropriate_Outcome_Measure_in_Lupus. Accessed March 11, 2016.

18. GlaxoSmithKline. BENLYSTA (belimumab). GSKsource. https://www.gsksource.com/pharma/content/gsk/source/us/en/brands/benlysta/pi/primary-endpoint.html. Accessed March 11, 2016.

19. SLEDAI-2K length of assessment in Toronto Lupus Cohort - personal communication. March 2016.

20. Touma Z, Urowitz MB, Gladman DD. SLEDAI-2K for a 30-day window. Lupus. 2010;19(1):49-51. doi:10.1177/0961203309346505.

21. Mikdashi J, Nived O. Measuring disease activity in adults with systemic lupus erythematosus: the challenges of administrative burden and responsiveness to patient concerns in clinical research. Arthritis Res Ther. 2015;17:183. doi:10.1186/s13075-015-0702-6.

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8 Appendix 1.External Cohort Comparison Report:

Double-click icon below to view embedded external cohort comparison report pdf.

External Cohorts Report

MILESTONES

MILESTONE GUIDANCE OR POLICY REQUIREMENT

FORECAST DATE MM-YYYY

Forecast Final Protocol Approval

Forecast GSK CSR Protocol Summary

FPA Actual + 30 days

Forecast Statistical Analysis Plan Approved

Forecast Statistical Analysis Complete

Forecast Final Study Report Complete

SAC Actual + 6 months

Forecast GSK CSR Results Summary Posting


Forecast Manuscript Submission


*complete removal of patient data listings may …...4 6.1.7 change from baseline of sdi organ...

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