advanced statistical considerations for early phase trials · 2018. 12. 14. · test for...
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Advanced Statistical Considerations for Early Phase Trials
J. Jack Lee, Ph.D. (李君愷)Professor, Department of Biostatistics
OutlinePhase I/II Designs– Eff-Tox design, Multc-lean design– BOP2 design
Bayesian Interim Monitoring – Toxicity Monitoring via posterior probability– Efficacy monitoring via posterior probability– Efficacy monitoring via predictive probability
Design for Identifying OBDRevolution of Immuno-oncology Trials Past, Present, and Future of Cancer TrialsSteps for Success– Keep it smart and simple– Need user-friendly software for the design & implementation
EFF-TOX: Phase I/II DesignDose finding based on efficacy/toxicity trade-offs (EFF-TOX)– Define a dose level x as acceptable if
– For example, Prob(Response rate > 0.3) > 0.1 & Prob(toxicity rate < 0.4) > 0.1
– Derive an efficacy toxicity trade-off contour on a two-dimensional outcome space
Thall and Cook, Biometrics 2004;60:684-693
Pr{ ( , ) | ) andPr{ ( , ) | )
E E E
T T T
x D px D p
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EFF-TOX: Dose Finding with Efficacy & Toxicity Trade-offs
Enroll patients in cohorts1. Treat the first cohort at the starting dose2. Observe the outcome, then, update the prob of efficacy and
toxicity3. Determine the acceptable doses among the pre-specified
doses4. Enroll the next cohort into the most desirable dose
according to the EFF-TOX tradeoff contour5. Stop the trial when there is no acceptable doses or reaches
max(N) 6. Otherwise, go to 2.
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Multc Lean: Toxicity and EfficacyPhase II study simultaneously controls both toxicity and efficacyA toxicity rate of 0.2 is acceptable but a rate of 0.4 is unacceptable. Assume that the information from the standard arm with a response rate of 0.3 and a toxicity rate of 0.2Early stopping for toxicity but not efficacyAssume the maximum sample size of the trial is 75.Provide the operating characteristics for a new treatment with:1. response rate =0.3 and a toxicity rate= 0.42. response rate =0.4 and a toxicity rate= 0.2
Thall, Simon, and Estey, Bayesian sequential monitoring designs for single arm clinical trials with multiple outcomes, Statistics in Medicine, 14:357-379, 1995).
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BOP2: A Bayesian Optimal Design for Phase 2Clinical Trials with simple & complex endpointsProvides a unified framework for phase II trials with simple and complex efficacy and toxicity endpoints.Explicitly controls the type I (and II) error rates.Is optimal by (i) maximizing power, given a fixed N and type I error; or (ii) minimizing the E(N|H0), given fixed type I and II error rates.Easy to use software is freely available to generate stopping boundaries, operating characteristics and protocol for the BOP2 design.
Zhou H, Lee JJ, Yuan Y. BOP2: Bayesian optimal design for phase II clinical trials with simple and complex endpoints. Stat Med. e-Pub 6/2017. PMID: 28589563.
BOP2 Design, ExamplesExample 1: A treatment is – futile if ORR ≤ 0.2; promising if ORR ≥ 0.4.
Example 2: A treatment – Fails if CR ≤ 0.15 or CR+PR ≤ 0.30.– Succeeds if CR ≥ 0.25 or CR+PR ≥ 0.50.
Example 3: A treatment– Fails if ORR ≤ 10% and PFS6 ≤ 20%.– Succeeds if ORR ≥ 30% or PFS6 ≥ 35%.
Example 4: A treatment is safe and efficacious if– Null: ORR ≥ 45% or toxicity rate ≤ 30%.– Alternative: ORR ≥ 60% and toxicity rate ≤ 20%.
Stopping Boundaries for BOP2 Design
Bayesian Toxicity Monitoring
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Video: Bayesian Toxicity Monitoring
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Stopping Boundaries
Operating Characteristics
Operating Characteristics
Trial Monitoring
Design for Identifying OBDFor molecularly targeted agents & immune agents, little toxicity may arise within the therapeutic dose range. The dose–response curves may not be monotonic.The goal is to find the optimal biological dose (OBD), which is defined as the lowest dose with the highest rate of efficacy while safe.Apply nonparametric and uses the isotonic regression to identify the optimal biological dose.Identify “admissible” with acceptable toxicities. Assign patients to the dose with highest efficacy probabilities.Repeat the process until the Max(N) is reached.
Revolution of Immuno-Oncology TrialsChoice of endpointsThe use of dose-expansion cohortIdentify predictive markersHow to combine with chemotherapy, radiotherapy and/or targeted agents?
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Choice of EndpointsResponse rate– Delay response
Response durationProgression-free survival– Initial false progression
Overall survivalImmune markers– Immune cell infiltration– TCR– Change of markers before and after treatment
Pembrolizumab Trial: KEYNOTE-001Pembrolizumab is a PD-1 inhibitor.First-in-human trial, initiated in 2011, to determine the RP2D in patients with advanced solid tumorsStriking responses in initially enrolled metastatic melanoma pts. Prompted an increase in sample size for an evaluation of overall response rate and disease control rate in melanoma patients.Additionally, potential activity in NSCLC prompted the addition of a cohort to assess overall response rate in lung cancer. As promising results were obtained with each additional cohort, the trial continued to be expanded.Ultimately over 1,200 patients were treated in this trial. – One cohort had 173 patients with unresectable or metastatic melanoma who were
randomized to two different doses of pembrolizumab. Efficacy results were sufficient for accelerated approval in September 2014 - just three years after the initiation of the first-in-human trial
– Subsequently, data from the lung cohorts led to approval in lung cancer as well as the approval of a companion diagnostic for tumor PD-L1 expression
Theoret et al. Expansion Cohorts in First-in-Human Solid Tumor Oncology Trials. CCR 2015
Mullard, Nature Reviews Drug Discovery 2016
Largest Phase I Oncology Trials
Mullard, Nature Reviews Drug Discovery 2016
Remarks for the Dose Expansion CohortNeed compelling rationale for having multiple expansion cohortsNeed to define the specific objective and choose the proper endpoints (toxicity and efficacy)Need to provide sample size justification– Need a statistical design when N ≥ 30. (reaching to the
sample size for a single-arm Phase II study)Is there an early stopping rule? – Toxicity, Futility, Efficacy
Discuss with regulatory agency early and oftenNeed to have an independent data safety and efficacy monitoring board
Identify Predictive Markers –Medical perspective
Treatment– Different PD-1 inhibitors– Different dose, schedule
Patient population– Demographics: age, sex, gender, smoking history– Disease state: histology, performance status, first-line or
prior treated (prior treatments)– Immune profile: PD-L1, IFN- gene signature– Molecular profile: tumor mutation burden (TMB),
microsatellite instability (MSI) Biomarker assay– IHC staining: different antibodies– NGS platforms
Identify Predictive Markers – Statistical perspective
Endpoint selection– Response rate, PFS, OS
Analysis method– Continuous marker, transformation, dichotomize– How to choose the cut-off point?– Multi-covariate analysis, model interactions
Synthesizing information– Meta-analysis, meta-regression, network meta-analysis
Multiple testing– Subset analysis, adjust for multiplicity– Validation
Internal, external, prospective
Finding the Right Biomarkers First-line immunotherapy of NSCLC− Merck, KEYNOTE-024 Trial: pembrolizumab vs. platinum-based chemo− PD-L1 (+): ≥ 50%, N=305
Reck et al., NEJM 2016
KEYNOTE-024 Trial
Reck et al., NEJM 2016
Checkmate 026 Trial
Checkmate 026: PFS by Tumor Mutation Burden
(>243 mutations) (<100; 100-242 mutations)
Treatment x TMB Interaction?
Checkmate 026: ORR by TMB and PD-L1
Objective Response Rate by PD-L1 Cut-point
Khunger et al., JCO Precision Oncology 2017
1% Cut-off
2.17
5% Cut-off
2.80
10% Cut-off
2.84
RE Model
0.2 1 5 10
Odds Ratio (log scale)
Atezolizumab, Besse
Atezolizumab, Spira
Atezolizumab, Spigel
Pembrolizumab, Garon
Pembrolizumab, Herbst
Pembrolizumab, Garon
Durvalumab, Rizvi
Avelumab, Gulley
Nivolumab, Borghaei
Nivolumab, Brahmer
Nivolumab, Rizvi
Nivolumab, Gettinger
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5
5
5
5
5
79
9
14
31
87
49
23
18
32
9
6
5
223
15
39
42
203
70
61
104
63
33
19
28
36
8
12
17
40
31
5
2
19
11
7
5
321
61
72
86
360
206
87
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64
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3.16 [2.06, 4.85]
4.57 [1.51, 13.84]
2.15 [0.91, 5.11]
3.73 [1.86, 7.50]
3.86 [2.55, 5.82]
4.65 [2.75, 7.86]
6.56 [2.36, 18.21]
1.56 [0.33, 7.30]
3.13 [1.64, 5.96]
1.59 [0.60, 4.21]
1.98 [0.59, 6.70]
1.07 [0.28, 4.10]
3.31 [2.66, 4.11]
Response No Response No
High PD-L1 LOW PD-L1Cutoff PctDrug and author
Odd Ratio [95% CI]
tau^2 estimate=0.0141 (se=0.0628), method:EBTest for Heterogeneity: Q(df=11)=11.8106, p_value=0.3780
Log OR estimate=1.196, se=0.1108, p_value<0.0001
Meta-analysis From 12 Trials
Data Taken from Grigg and Rizvie, J for Immu Therapy of Cancer 2016
1% 5% 10% 25% 50% 75%
PD-L1 Cut-Off
Obj
ectiv
e R
espo
nse
Rat
e
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.240.27
0.32
0.420.46
0.54
N= 637 N= 242 N= 122 N= 138 N= 160 N= 72
Objective Response Rate by PD-L1 Cut-point
Adapted from Aguiar Jr et al., Immunology 2016
Aggregated Data From 11 Trials
Objective Response Rate by PD-L1 Cut-point
Grigg and Rizvie, J for Immu Therapy of Cancer 2016
Grigg and Rizvie, J for Immu Therapy of Cancer 2016
Tumor Mutation Burden as A Marker
(Durable Clinical Benefit) (No Durable Benefit)
Rizvi, Science 2015
Rizvi, Science
IFN- Gene Expression as A Marker(in Head and Neck Squamous Cell Carcinoma)
Ayers, et al., JCI 2017
Ayers, et al., JCI 2017
Biomarkers SummaryBiological, mechanism-based biomarkers are useful for immuno-oncology drug development.– Preclinical testing for biomarker data
Cell lines, syngeneic mouse model, PDX, etc.
No shortage of methods for biomarker discovery– Select functional form of the biomarker
Continuous, dichotomizing, cut-point selectionCheck Martingale residual, with or without adding other covariates
– Variable selection, Multiple regressionMulti-covariable analysis
– Control type I error and/or false discovery rateValidation is the key– Many are called; few are chosen
Internal, external, independent validation
Past, Present, and Future of Cancer Trials
Past: One trial, one drug, one patient population at a time expensive, high failure rate – Disjoint process, non-targeted agents, no patient selection
based on markers– Fixed design, infrequent interim analyses– Rush into Phase III too early
Present: biomarker integrated– Biomarker-based patient selection and drug matching– All comers, “master protocol” trials, platform trials
Future: smart trials– More adaptive designs in Phase I and Phase II trials– Smaller, more focused Phase III trials with higher success
rates.
3 Key Elements for Contemporary TrialsDiscovery– Keep your eyes open. Look for interesting patterns.– Be aware of multiplicity issues and false positive findings.– Exploratory: Hypothesis generating
Validation– Internal validation, external validation, prospective validation– Confirmatory: Hypothesis testing
Being Adaptive– We learn as we go.– Continuous learning: cycle between discovery and validation– Platform trials, take all comers– Perpetual trials, continuous learning and improving
Implement and make a difference!
Steps for SuccessKeep it simple and smart (KISS)Be adaptive– We learn as we go.– Frequent decision making on treatment assignment,
early stopping on toxicity, futility, and/or efficacyNeed user-friendly software for the study design and conduct
http://www.trialdesign.org/
Clinical Trial Design Software
http://www.trialdesign.org/
https://biostatistics.mdanderson.org/softwareDownload/
80+Programs Freely Available!
https://biostatistics.mdanderson.org/softwareOnline/
Secret of Life
Avoid brute force! Work with a statistician!
Be adaptive! Be smart!
Let’s do it !
Implement and make a difference!
Team Work!
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