clinical study design henrik ekberg, md, phd

Clinical Study Design

Henrik Ekberg, MD, PhDMalmö, Sweden

Associate Editor: American Journal of Transplantation 2003-

Editorial Board Member:Transplantation 2004 -

Transplant International 2004 -Clinical Transplantation 2008 -

Journal of Transplantation 2008 -

Guangzhou October 9, 2010

Rejection of submitted manuscript- various reasons

• Rejected on priority grounds: Maybe a good study – but not a topic of interest, or done before

• Rejected, not allowed resubmission:– a bad study; design problems, cannot be re-

written in a good way

• Rejected but allowed resubmission:– no serious design problems, interesting topic,

but needs to be rewritten for language, discussion, figures, tables, etc.

Study design alternatives

• Retrospective studies = Using medical charts of existing data• Uncontrolled • Case-controlled• Hypothesis generating

• Prospective studies= Protocol directives for Rx and F/u• Uncontrolled, one-arm, pilot• Randomized Controlled Trial (RCT)• Hypothesis testing

Clinical study design phases

• Phase 1• Drug action, metabolism, PK, PD, safety

• Phase 2• Limited (un)controlled study for efficacy and safety

• Phase 3• Large randomized multicenter study• Determine efficacy and safety for FDA and EMEA

• Phase 4• After drug release: new uses of the drug• Marketing

HypothesisAppropriate populationClinically relevant achievementAdequately-poweredEnd pointsComparison group (placebo)RandomizedDouble-blindIntent-to-treat analysisProtocolAnalysis plan

Key Elements of Trial Quality

Experimental HypothesisMay be based on a pilot or retrospective study or on hopes for a new drug

Drug A > drug B (or placebo)with regards to …

Null hypothesis (H0): A < B, A > B (no difference)A < B (non-inferiority)


Appropriate population

Include: Normal risk kidney transplant recipientsfrom living or deceased donors

Exclude: High risk patients, such asPRA > 20% (50%?)Retransplants (all?)High donor age ?Expanded donor criteria?Cold ischemia time ?HLA- DR mismatch ?


With one-year graft survival > 90%and acute rejection rates < 20%

The Success

With one-year graft survival > 90%and acute rejection rates < 20%

we have a high level of successand further improvement is difficult to

achieve and demonstrate

we need very large studies!

The Problem

Primary end pointThe parameter on which 1. the hypothesis is based, to be verified or rejected2. the sample size is calculated

Secondary end pointsAdditional parameters which may1. describe the patients, events and results2. be used for formulations of new hypotheses

End Points and Sample Size

1. Select the primary end point

2. Clinically relevant achievement regarding end point = Difference between control and experimental groupse.g.: GFR increased by 10 ml/min

AR rate reduced by 10%

3. Determine the number of patients in each group needed to verify that the difference between the groups most likely is true (<5% risk of mistake).

4. With a certain power and p-value.


“We chose to study 313 patients in each group in order to have 80% power of detecting a 33% reduction in AR rate from a baseline rate of 30% with a significance level of 0.05”.

End point: Acute RejectionClinically relevant achievement:

33% reduction (from 30% to 20%)Power: 80%Significance level: 5%Therefore:

Number of patients in each group: 313



= p-value = 0.05; means a 5% risk of obtaining a group difference by chance (although there is no true difference).

= 0.20 means a Power of 80%; that is 80% chance of obtaining a group difference and 20% risk of missing it (when there is a true difference).

P1 = 0.30 and ∆ = 0.10 (33% of p1)





P1 = 0.30 and ∆ = 0.10 (33% of p1)

End Points and Sample SizeQuestion:If there is a true difference between the groups and

we do 100 studies with 313 patients in each groupHow many studies will result in a group difference,

that is at least a 33% reduction of AR?

1. 5 studies2. 20 studies3. 80 studies4. 95 studies




P1 = 0.30 and ∆ = 0.10 (33% of p1)

End Points and Sample Size80 studies will show a significant differenceand 20 studies will not.

Comment:20% risk of not seeing a true differenceis quite high




P1 = 0.30 and ∆ = 0.10 (33% of p1)

End Points and Sample SizeQuestion:If there is not a true difference between the groups and

we do 100 studies with 313 patients in each group.How many studies will result in a group difference?

1. 5 studies2. 20 studies3. 80 studies4. 95 studies




P1 = 0.30 and ∆ = 0.10 (33% of p1)

End Points and Sample SizeQuestion:If there is not a true difference between the groups and

we do 100 studies with 313 patients in each group.How many studies will result in a group difference?

5 studies will show a group differencealthough this is not true


P-value = 5%; The risk of seeing a difference which is not true

Power = 80%; The chance of seeing a difference which is true

P1 = 0.30 and ∆ = 0.10 (33% of p1)


p=0.05

Sample Size for Acute Rejection

AR Treatment

AR

Control (P1)

Power Sample size

0.20 0.30 80% 313

p=0.05


AR Treatment

AR

Control (P1)

Power Sample size

0.20 0.30 80% 313

0.15 0.20 80% 700

p=0.05


AR Treatment

AR

Control (P1)

Power Sample size

0.20 0.30 80% 313

0.15 0.20 80% 700

0.15 0.20 90% 954

We need to do large multicenter studies !!!

Question:The primary end point (PEP) and 10 secondary end points (SEP) were analysed; SEP in two ways each.The PEP was NS, one of the SEP was stat sign (P<0.05).

Why is the analysis more reliable for PEP than SEP?Is this significant result of the SEP reliable?10 x 2 = 20 tests What is the probability of a “significant finding” by chance?


The trap of multiple tests

No. of independent tests

2 5 10 20 50

Probability of one or more p < 0.05 by chance

10% 23% 40% 64% 92%

To keep = 0.05accept as significantonly p less than

The trap of multiple tests

No. of independent tests

2 5 10 20 50

Probability of one or more p < 0.05 by chance

10% 23% 40% 64% 92%

To keep = 0.05accept as significantonly p less than

0.025 0.010 0.005 0.002 0.001

Use p = 0.05 / no. of tests

Clinical End Points

We want to achieve improvement inpatient survival and graft survival

These are the Clinical end points

Five cadaver kidney transplant recipients received azathioprine

One patient survived 365 days, becoming the first successful cadaveric transplant

Uncontrolled Trial: Patient Survival (n=5)

Murray, et al. New Engl J Med 1963; 268:1315

1-year graft survival CsA.......72% Aza........52%

1-year patient survival CsA.......94% Aza........92%

European Multicentre Trial Group. Lancet 1983; 2:986

p=0.001

RCT: Graft & Patient Survival (n=232)

NS

Acute rejection is associated with graft survival

Acute rejection became the surrogate end point for graft survival

Where Did We Go From Here?

Acute rejection at 6 mo. MMF 2g...........20%

MMF 3g...........17%

Pla/Aza.............41%

1-year graft survival MMF 2g...........90%

MMF 3g...........89%

Pla/Aza............88%

Halloran, et al. Transplantation 1996; 63:39

p<0.01

RCT: Acute Rejection (n=1493)

NS

Conclusion of MMF trials:

“Acute rejection was reduced but graft survival was not improved”

Was this true - or a question of insufficient power of the study?

What difference in graft survival should have been expected?

Where Did We Go From Here?

Sample size and power to verify true differences in graft survival of 4% or 5%.

Graft survival in treatment groups

Difference in Graft survival

Sample size at 80% power

Power at sample size 150

86 % 90 % 4 % 1 037 19 %

75 % 80 % 5 % 1 091 18 %

Ekberg H. Transpl Rev 2003; 17: 187

Surrogate Endpoint Definitions

Clinical endpoint:A characteristic or variable that reflects how a patient feels, functions or survives.

Surrogate endpoint:A biomarker that is intended to substitute for a clinical endpoint, and predict clinical benefit …

Biomarkers Definitions Working Group. Clin Pharmacol Ther 2001; 69:89

Risk factors after transplantationAcute rejectionGraft functionNew onset of diabetes mellitusCholesterol levelsTreatment failure (drug toxicity)Malignancy

Do they predict graft or patient survival?

Risk factors and potential End points

Possible Surrogate Endpoints

Acute rejection Acute rejection + 1/Cr return to baseline 1-year graft function Composite end point

Association or Prediciton ?

Acute Rejection with 1/Cr return to baselineTransplants 1995–2002

Log-rank P value for equality of strata ≤0.0001.Meier-Kriesche et al. ATC 2003.

Time Posttransplantation (mo)

1.0

Graft Survival

(%)

0 6 12 18 24 30 36 42 48 54 60 66 72

0.9

0.8

0.7

0.6

0.5

0.4

AR-1/SCr worse than 5% from baseline49.4%

n = 55,092n = 4,061n = 2,782

n = 22,212n = 2,669n = 1,455

n = 2,891n = 414n = 221

AR-1/SCr within 5% from baseline

73.4%

73.1%

No acute rejection

Predictive Quality for Graft Loss: AR vs. AR Without Return to Baseline

6 years

2 years

Follow-up

38.527.6

15.89.2

Positive Predictive Value

Acute Rejection No Return to

BaselineAcute

Rejection

Meier-Kriesche et al. ATC 2003.

Conclusion: AR and AR with return to baselineare associated but not predictive of graft survival

“Post-transplant Renal Function at 1 Year Predicts Long-Term Kidney Transplant Survival”

0

20

40

60

80

100

0 12 24 36 48 60

Months Posttransplantation

<1.0

1.0 - 1.5

1.6 - 2.0

2.1 - 2.5

2.6 - 3.0

>3

N = 61,157

Graft Survival

(%)

Hariharan S et al. Kidney Int. 2002; 62: 311.

ROC Plot for 7-Year Overall Graft Loss From 1-Year Creatinine Baseline Level AUC = 0.624

Sensitivity

1 - Specificity

0.0 1.00.90.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

3.02.5

2.32.1

2.01.9

1.81.7

1.61.5

1.41.3

1.21.01.1

ROC = receiver operator curve.

H-U Meier-Kriesche

Prediction Diagnostics for Seven Year Overall Graft Loss from One Year Creatinine Level

Patient population: Adult first transplant recipients from USRDS database

after 1988 with minimum seven years follow up

Prediction Diagnostics

Sensitivity Specificity PPV NPV

Creatinine Cutoff Level

1.6 62% 55% 53% 64%

1.8 48% 71% 58% 62%

2.0 36% 82% 63% 61%

H-U Meier-Kriesche

Possible Surrogate Endpoints

Acute rejection

Acute rejection + 1/Cr return to baseline

1-year graft function

Composite end point

Composite end point (CEP)

1,389 KTx at Univ of Minnesota 1985-1997

Creat at 1 year (Cr12)

Cr12 <1.0 to >3.0 -> 10 yr GS from 75% to 25%

Suggested Composite End Point:

Graft loss < 12 mo. or Cr12 > 2.0Reduction of CEP incidence by 33%626 patients in total needed in such study

Paraskevas et al Transplantation 2003; 75: 1256


CEP definition:Occurrence of at least oneAcute rejection, Graft loss, Death or S-Creat > 1.5

UNOS data base 1995-2000: 59,000 patients61.2% met the CEP - Margin for improvement- Less number of patients needed

Siddiqi et al ATC 2003; #1160Hariharan et al AJT 2003; 3: 933


CEP:Not a surrogate end point – no predictionNot a clinical end point – incl ‘surrogate’ factors

Weighted score:Death 1.0 x proportionGraft loss 0.5 x proportionAcute rej 0.25 x proportionS-crea>1.5 0.25 x proportion

Hariharan et al AJT 2003; 3: 933

Clinical end point (short term only)

Alternatively;Clinical end point (“how the patient functions …”)without prediction of long-term patient or graft

survival

e.g. GFR (Cockcroft-Gault formula) at 12 mo.Symphony study e.g. New Onset of Diabetes After Transplantation

(NODAT) according to American Diabetes Association (ADA) definitions

Conclusions on End Points

What are the best end points?

Acute rejection

Acute rejection + 1/Cr return to baseline

1-year graft function

Composite end point

NODAT

HypothesisAppropriate populationClinically relevant achievementAdequately-poweredEnd pointsComparison group RandomizedPlacebo-controlledDouble-blindIntent-to-treat analysisProtocolAnalysis plan


Question:We are designing a study on CNI

nephrotoxicity and are discussing the treatment of the control group. It was decided to give them CsA with trough levels 200-400 ng/ml first 2 months and then 100-200 ng/ml months 3-12.

OK?

The Comparison Group

The benefits, risks, burdens and effectiveness of a new method should be tested against those of the best current prophylactic, diagnostic, and therapeutic methods.

World Medical Association Declaration of Helsinki

The Comparison Group

The new drug or method should hypothetically and potentially be better than the best known current treatment(= standard of care)- but not yet proven to be so

The Study Group

Placebo vs Study Drug Study drug in addition to the best current regimene.g. placebo vs daclizumab

Old Drug vs New DrugEither drug in addition to the best current regimene.g. Aza vs MMF

Controlled trial

Random Assignment of TreatmentParameters associated with outcome should be similarly distributed between study and comparison groups

Methods for example: computerized and via telephone1:1 or 2:1Stratification (per center or LD/DD)

Randomized Controlled Trial

Double BlindPhysician not knowing which treatmentPatient not knowing

Problems: drug administrationdrug monitoring

Labs and visits the same in both groupsSometimes extra blood sampling in controls (ethics?)

The Blind Treating The Blind

ITT analysis – the Standard method= All participating patients are included Does not exclude treatment failuresConclusion: “With this intention, we had the results ...”

Limitation of the ITT AnalysisIn a long-term study (e.g. 3 yrs), many patients would

have switched therapy or been withdrawnPhysicians regard the fate of the patient more

important than the study-> Reduced differences between treatment groups

Intention-to-Treat Analysis

Per Protocol (PP) Analysis= On-treatment analysisEmphasis on the positive results of treatmentExcludes premature withdrawals (“failures”)

Limitation of the PP analysisConclusion: “Only in successful cases, we had these results...”“Only patients who could follow this protocol, …”-> Seriously biased results when excluding failures

Per Protocol Analysis

Synopsis and Protocol

SynopsisA short summary of the study protocolUsed to invite investigators to participate

ProtocolA detailed description of all relevant aspects of the studyUsed to make sure all centers perform the study

correctlyUsed for approval of Ethical Committee and Health

Authorities

Patient Information and Consent

Analysis Plan

A detailed description of all analyses that are planned; statistical methods, outlines of tables and graphs

Including:Primary end point to verify or reject the null hypothesis Secondary end points to further describe the data and

formulate new hypotheses

Secondary analyses (ad hoc, made after viewing the results and not part of the analysis plan) should be avoided

Interim analyses - confidentially for Data Safety Monitoring Board (DSMB). To report in public interim results during the study should not be done!

Further Reading

A Uniform Clinical Trial Registration Policy for Journals of Kidney Disease, Dialysis and transplantation

Couser WG, AJT 2005; 5: 643 www.clinicaltrials.gov

Design and Analysis of Clinical Trials in Transplantation

Schold JD, AJT 2008; 8: 1779

clinical study design henrik ekberg, md, phd

Documents

surrogate end

best end points

good study

selectthe primary end

composite end point

retrospective study

symphony study

bad study design problems