Oncology Biostatistics Design strategies to assess benefit for biomarker sub-populations in Phase III clinical trials Bharani Dharan, Ekkehard Glimm Joint Statistical Meeting, Denver July 31, 2019

Outline Background Factors impacting biomarker study designs Types of biomarker designs Simulation Results Summary

Background: Biomarker based Phase III designs A confirmatory trial with a biomarker subpopulation Multiple populations of interest : All patients, Biomarker positive (B+), Biomarker negative (B-) Multiple statistical comparisons involved due to multiple populations Overall type 1 error rate needs to be controlled at one-sided 2.5% level Role of B+ subgroup is key to assess any differential effect between the biomarker subgroups Freidlin B, Sun Z, Gray R, et al: Phase III clinical trials that integrate treatment and biomarker evaluation. JCO 31:3158-3161, 2013 Goteti S, Hirawat S, Massacesi C, Dharan B. Some practical considerations for phase III studies with biomarker evaluations. JCO 32:854–855, 2014

Factors that can impact the study design Biomarker prevalence, targeted effect, biomarker assay Prevalence of the biomarker subgroup If B+ comprises a higher percentage of patient population, it is expected that treatment effect in “all patients” will be influenced by treatment effect in B+ Targeted treatment effect in biomarker subgroups and all patients A conservative targeted effect can result in over-powering of the endpoints. Assay to determine the biomarker status Treatment effect needs to be demonstrated by CDx assay in addition to clinical trial assay Assay sensitivity and specificity is crucial Minimize unknown status or randomize only the known biomarker status patients

Biomarker based Phase III designs Traditional All patients design Flexible efficient testing procedures e.g. alpha split approach tailored to match the study objective Treatment Enrollment in All patients (stratified by Biomarker status) Primary analysis (All, B+) Control

Biomarker based Phase III designs Biomarker Stratified design Allows to ‘independently’ test the biomarker positive and negative subpopulations Treatment Primary analysis (B+) B+ Randomization Control Biomarker status determination Treatment Primary analysis (B-) B- Randomization Control SCREENING Treatment & Follow up

Biomarker based Phase III designs Adaptive enrichment design Allows selection of the population (All patients or biomarker positive) based on pre-specified decision rules at the interim analysis Stop for futility Treatment Enrollment in All patients (stratified by biomarker status) Pre-specified decision rules at interim Continue in Full population Control Stop recruitment in B- and Enrich B+ subpopulation

Traditional All patient design: Simulation set-up Study enrolled ‘all patients’ which include biomarker positive and negative sub-populations Primary endpoints: Progression-free survival (PFS) in All patients and biomarker positive Key secondary endpoints: Overall survival (OS) in All patients and biomarker positive Gatekeeping approach to test the multiple endpoints Assumptions: Target hazard ratio for PFS: 0.67 in all patients and 0.60 in B+ sub-population Target hazard ratio for OS: 0.75 in all patients and 0.625 in B+ sub-population Accrual rate: 30 patients/month with ~40% B+

Traditional design: Statistical testing approach Alpha split approach tailored to the hypotheses of interest Several testing strategies are explored Scenario 1: More alpha for overall and less alpha for biomarker +: 2%, 0.5% Scenario 2: Equal alpha split between overall and biomarker +: 1.25%,1.25% Scenario 3: Less alpha for overall and more alpha for biomarker +: 0.5%, 2% . Scenario 1: : Endpoint meets success : Endpoint fails to meet success

Statistical testing strategy Alpha split approach tailored to the hypotheses of interest Scenario 2 Scenario 3

Statistical impact of the three testing strategies Scenario 1 has the optimal power for both OS and PFS; Scenario 3 is optimal from sample size; PFS adequately powered for all scenarios Lower sample size for Scenario 3 90% power for OS for “All patients” in Scenario 1 Scenario 1: More alpha for All patients and less alpha for B+: 2%, 0.5% Scenario 2: Equal alpha split between All patients and B+: 1.25%,1.25% Scenario 3: Less alpha for All patients and more alpha for B+: 0.5%, 2%

Testing strategy for biomarker negative Decision rule for B- Biomarker negative sub-population will be assessed to determine whether the treatment effect in All patients is driven solely by biomarker positive subpopulation or by both positive and negative subpopulation Should there an alpha allocated to test the biomarker negative? or Will a pre-defined clinically relevant threshold based on a decision rule suffice? E.g. Hazard ratio <0.67 and probability(HR<1)>97.5%

Stratified design Feasible option when either B+ or B- is of interest Treatment Primary analysis (B +) B+ 1:1 Control Treatment Primary analysis (B -) B- 1:1 Control A feasible option when either B+ or B- is of interest Ensures unknown status doesn’t confound the interpretation Better control of prevalence rate and can avoid over-powering resulting from traditional design Gatekeeping approach required for testing B+ and B– subgroups

Adaptive enrichment study design Adaptation decision on enrichment at interim analysis Stop for futility Patient Population: (stratified by biomarker status: + and -) Treatment Pre-specified decision rules at interim analysis Continue in All patients population Control Stop recruitment on B- and Enrich B+ subpopulation

Adaptive enrichment design: Decision Rules at Interim analysis Bayesian predictive power (PP) & Posterior probability for futility Decision Rules PPAll < x & PPB+ < y Stop for futility PPAll ≥ x & PPB+ < y Continue in All patients population Interim analysis PPAll ≥ x & PPB+ ≥ y & rule in B-* not met PPAll < x & PPB+ ≥ y Stop recruitment on B- and Enrich B+ subpopulation PPAll ≥ x & PPB+ ≥ y & rule in B-* met PPAll: probability to meet success in either (or both) PFS in the full population or PFS in the B+ subpopulation at final analysis given interim data when study continued in full population PPB+: probability to meet success in PFS in the B+ subpopulation given interim data when adapted to B+ subpopulation *rule in the B- (Posterior probability P(HR>HR1)>z )

Adaptive enrichment design: Methodology Multiplicity issues Testing in All patients & B+ populations Two looks; Interim -Adaptation, Futility; Final - Efficacy analysis Closed testing procedure Hochberg p-value qi for intersection hypothesis H0F ∩ H0S at stage i qi=min{2 min(piS, piF), max(piS, piF)} At final analysis If trial continues in full population Tests in F and S of equal interest (H0F & H0S co-primary) Elementary hypotheses at stage 2: Reject H0F ∩ H0S if C(q1, q2)≥c2 then: reject H0F if C(p1F, p2F) ≥c2 & reject H0S if C(p1S, p2S) ≥c2 If trials is adapted to the B+ subpopulation Reject H0F ∩ H0S if C(q1, p2S)≥c2 then Reject H0S if C(p1S, p2S) ≥c2 *F refers to “All patients” & S refers to biomarker positive subpopulation

Adaptive enrichment design: Methodology* Inverse normal method p1 is the p-value based on log-rank z-statistic Z1 from stage-1. p2 is obtained from the difference in log-rank statistics z2-z1, where z2 is the log-rank z-statistic based on the cumulative data at stage-2. Independent p-values from 2 stages combined: inverse normal method (Lehmacher & Wassmer 1999, Cui, Hung & Wang 1999) Pre-specified fixed weights based on “All patients” information fractions: 𝑤1= (𝑛1/𝑁) 1/2 𝑤2= (𝑛2/𝑁) 1/2 , 𝑤1 2 + 𝑤2 2 =1, 𝑛𝑖/𝑁 event fraction at stage i *Brannath W et al. (2009) Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology, Stats. Med; 28: 1445-1463 𝐶 𝑝1,𝑝2 =𝑤1ф −1 (1−𝑝1)+𝑤2 ф −1 (1−𝑝2)

Adaptive enrichment design Simulation set-up Prevalence of biomarker positive 30% 40% 50% Posterior probability in the biomarker negative subpopulation: Probability cutoff: 0.75 Hazard Ratio cutoff: 0.80 Predictive power cutoff (Full population, biomarker positive subpopulation): (0.35, 0.35) Hazard ratio scenarios for biomarker positive and negative Sample size of 524 patients; Interim analysis at 125 PFS events; Final analysis at 374 events in the All patients population and 281 events if enriched to the B+ subpopulation

Adaptation decision at Interim analyses: Simulation results for different prevalence rates Hazard ratio Probability to B+ B- Stop for futility (%) Go with All (%) Go with B+(%) 30% 40% 50% 0.5 0.4 0.2 100 99.4 99.3 0.8 3 2 72 74 75 25 24 23 1.0 6 4 33 38 42 61 58 56 0.67 7 88 87 85 8 13 12 66 67 21 20 19 16 29 31 36 50 48 68 59 63 71 14 18 15 19

Overall trial power: Simulation results for different prevalence rates Hazard ratio Unconditional Power B+ B- Overall power Prob. success in All patients population Prob. Success in B+ sub-population 30% 40% 50% 0.5 100.0 99.6 99.8 99.7 99.4 99.2 95.4 98.0 0.8 94.7 97.5 68.7 72.4 74.8 92.3 96.7 97.9 1.0 91.2 95 97.6 21.2 32.0 39.4 91.1 94.9 0.67 89.2 89.5 89.8 84.0 83.8 82.4 57.1 67.4 76.1 72.3 76.9 79.4 51.5 57.3 59.4 57.5 67.2 73.4 62.0 66.4 72.2 10.3 15.0 23.2 60.9 65.2 71.4 20

Conditional Power: Simulation results for different prevalence rates Hazard ratio Conditional Power B+ B- Prob. Success / go All Prob. Success in All /go All Prob. Success in B+ /go All Prob. Success in B+/go B+ 30% 40% 50% 0.5 100.0 100 99.9 95.4 98.4 99.8 0.8 97.2 99.3 99.7 94.9 99.5 93.9 98.2 1.0 91.5 96.9 99.4 64.3 84.7 95.0 91.1 96.6 0.67 95.7 96.4 96.5 95.6 96.3 59.1 70.9 80.4 89.1 91.6 94.6 80.7 86.4 78.2 85.0 88.1 58.2 71.9 80.3 93.4 56.1 66.1 78.1 35.0 47.8 64.7 52.1 62.4 75.8 91.3 91.4 92.3 21

Hazard Ratio plot No treatment effect (HR=1)in both B+ and B- sub-populations Hazard ratio All population Hazard ratio biomarker negative Hazard ratio biomarker positive Hazard ratio biomarker positive + Go B+ X Futile O Go All Decision at interim Probability Go All patients 15.7% Go B+ 21.2% Stop for futility 63.1%

Hazard Ratio plot Treatment effect (HR=0.67) only in B+ sub-population Hazard ratio All population Hazard ratio biomarker negative Hazard ratio biomarker positive Hazard ratio biomarker positive + Go B+ X Futile O Go All Decision at interim Probability Go All patients 29.4% Go B+ 49.9% Stop for futility 20.7%

Hazard Ratio plot Treatment effect (HR=0 Hazard Ratio plot Treatment effect (HR=0.67) in both B+ and B- sub-populations Hazard ratio biomarker negative Hazard ratio All population Hazard ratio biomarker positive Hazard ratio biomarker positive + Go B+ X Futile O Go All Decision at interim Probability Go All patients 87.8% Go B+ 5.9% Stop for futility 6.3%

Adaptive enrichment design: Operational elements Biomarker prevalence, DMC and enrollment hold Biomarker prevalence can impact the decisions Data Monitoring Committee (DMC) should be involved in the decision making for adaptation Following DMC decision, randomization scheme will need to take into account the impact on randomization and stratification Investigators should be informed of the possible actions and events in the course of the study which could result from the decision making in this trial Follow-up of ongoing biomarker negative patients in case the study adapts to biomarker positive sub-population at the time of interim analysis Enrollment hold during DMC review for adaptation

Summary Prevalence of biomarker groups plays a crucial role in the study design and statistical testing sequence Should be continuously monitored during the study Inclusion/exclusion of ‘unknowns’ should be carefully considered at the study design stage Designs such as gate-keeping procedures, adaptive designs or stratified designs can be used depending on the strategy

Acknowledgments Frank Bretz Patrick Urban Nathalie Fretault Emmanuel Zuber Sasikiran Goteti Samit Hirawat Cristian Massacesi Willi Maurer Sylvie Le Mouhaer Kannan Natarajan Yuanbo Song

References Bretz F, Maurer W, Brannath W, et al: A graphical approach to sequentially rejective multiple test procedures. Stat Med 28:586-604, 2009 Brannath W, Zuber E et al: Confirmatory adaptive designs with Bayesian decision tools for a targeted therapy in oncology, Stats. Med; 28: 1445-1463, 2009 Freidlin B, Sun Z, Gray R, et al: Phase III clinical trials that integrate treatment and biomarker evaluation. JCO 31:3158-3161, 2013 Goteti S, Hirawat S, Massacesi C, Fretault N, Bretz F and Dharan B: Some practical considerations for phase III studies with biomarker evaluations. JCO 32:854–855, 2014 Rothmann et al, Testing in a Pre-specified Subgroup and the Intent-to-Treat Population: DIA, 175-179, 46(2), 2012