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Joint Statistical Meeting Evaluation of Type 1 Error in a 2-in-1 Adaptive Phase 2/3 Design with Dual-Primary Endpoints in Oncology Studies Li Fan and Jing Zhao Merck & Co., Inc Joint Statistical Meeting Denver, CO Jul 30, 2019

Outlines Introduction/Motivation Methods Results Conclusion

Introduction 2-in-1 Phase 2/3 Adaptive Design* Advantages: FAST Introduction 2-in-1 Phase 2/3 Adaptive Design* Advantages: Limited investment initially Can quickly move to Ph 3 if Go Criteria are met Ph 2 patients are included in Ph 3 analysis for registrational intent with reasonable multiplicity adjustment No penalty needs to be paid for multiplicity control as long as the correlations for the 3 test statistics satisfy ρXY≥ρXZ Y>w? Keep as a Phase 2 No X>C? Phase 2 trial Z>w? Expand to Phase 3 Yes * Chen C, Anderson K, Mehrotra DV, Rubin EH and Tse A. A 2-in-1 Adaptive Phase 2/3 Design for Expedited Oncology Drug Development. Contemporary Clinical Trials 2018; 64:238-242.

Introduction 2-in-1 Phase 2/3 Adaptive design? Why do we need it? -- In the situation of the lack of Ph 2 support in oncology development, 2-in-1 design can mitigate the risk for Ph3. Go/No-Go decision built in 2-in-1 design --Decision criteria needs to be set up for continuation as Ph 2 or expansion to Ph 3

Introduction How to demonstrate Type 1 error well-controlled? Let X, Y, Z are the standardized test statistics corresponding to the primary endpoint for adaptive decision, smaller trial and larger trial, respectively. Let c be the cut-point for the decision making 𝑇𝑦𝑝𝑒 𝐼 𝑒𝑟𝑟𝑜𝑟= Pr 𝑋<𝑐,𝑌>𝑤 + 𝑷𝒓 𝑿≥𝒄,𝒁>𝒘 ≤ Pr 𝑋<𝑐, 𝑌>𝑤 + 𝑷𝒓 𝑿≥𝒄,𝒀>𝒘 , 𝒊𝒇 𝝆 𝑿𝒀 ≥ 𝝆 𝑿𝒁 = Pr 𝑌>𝑤 Overall Type I error is controlled at the target alpha level e.g. w=1.96 to ensure the Type 1 error to be 2.5%

Introduction Validation of correlation assumption ρXY ≥ ρXZ (Cited from Table 1 of Chen 2018*) In the most cases, the assumption holds; but if the assumption does not hold, need to calculate Type 1 error Scenarios for endpoints Implication to the assumption X, Y, Z are same Holds X and Y are same, Z different X and Z are same, Y different May not hold Y and Z are same, X different Holds when 𝜌 𝑋𝑌 ≥ 0 and 𝜌 𝑋𝑍 ≥0 X, Y, Z are different May hold

Motivation It is illustrated a general idea for the case when there is single primary endpoint/hypothesis in Phase 3 portion of 2-in-1 Phase 2/3 adaptive design [Chen 2018]. How to demonstrate Type 1 error well-controlled with extensions from the original proposal? Two extensions from Chen’s paper: Dual-primary endpoints in the Phase 3 portion Group sequential test in the Phase 3 portion

Method Extension 1: dual-primary hypotheses/endpoints in the Phase 3 portion Assume: X, Y and Z are test statistics for same endpoints, while S is different. Test statistic Analysis X expansion from Phase 2 to Phase 3 Y primary analysis of the Phase 2 portion Z dual-primary analyses of the Phase 3 portion S

Pr (X<𝑐, reject H0 for Y in Ph2) Method Assign α=2.5%(one-sided) to either Phase 2 or 3. Allocate α1 = x% for one of the dual-primary testing and α2 = (2.5-x)% for the other testing as the initial setting in Phase 3 portion. Under the different scenarios of the correlations, calculate 𝐓𝐲𝐩𝐞 𝐈 𝐞𝐫𝐫𝐨𝐫, under H0 (assume True HR =1 for Y and =(1, 1) for Z and S), derived by Pr (X<𝑐, reject H0 for Y in Ph2) + Pr (X ≥𝑐, reject H0 for Z or S in Ph3)

Method Evaluate the impact of the correlation (α1=α2=1.25%) Evaluate the impact of alpha splitting in Phase 3 ρXY ρXZ/ρXS/ρZS SC1 SC2 SC3 0.5 0.4/0.2/0.3 0.4/0.1/0.2 0.4/0.05/0.1 0.7 0.5/0.3/0.4 0.5/0.2/0.3 0.5/0.1/0.2 SC=Scenario Scenario α1 α2 SC1 1.25% SC2 0.50% 2.00% SC3 1.00% 1.50%

Results Overall Type 1 error is well-controlled at 2.5% under different scenarios of the correlations.

Results Overall Type 1 error is well-controlled at 2.5% under different alpha splitting for the dual-primary in the Ph3.

Method Extension 2: Group Sequential Test in Phase 3 Assume: X, Y and Z are test statistics for same endpoints. Test statistic Analysis X expansion from Phase 2 to Phase 3 Y primary analysis of the Phase 2 portion Z1 interim analysis of the Phase 3 portion Z2 final analysis of the Phase 3 portion

Method Assign α=2.5%(one-sided) to either Phase 2 or 3. Allocate α1 and α2 for the interim and final test under different scenario with combination of information fraction and α spending functions. Under the different scenarios, calculate 𝐓𝐲𝐩𝐞 𝐈 𝐞𝐫𝐫𝐨𝐫, under H0 (assume True HR =1 for Y and =(1, 1) for Z1 and Z2), derived by Pr (X<𝑐, reject H0 for Y in Ph2) Pr (X ≥𝑐, reject H0 for Z1 at interim of Ph3) + Pr (X ≥𝑐, Not reject H0 for Z1 at interim of Ph3, But reject H0 for Z2 at final of Ph3) +

Hwang-Shih-DeCani (γ) Method Evaluate impact of the information fraction with using O’Brien-Fleming α spending function Evaluate impact of α spending function with using the information fraction of 0.8 Information fraction 0.5 0.6 0.7 0.8 α spending function O’Brien-Fleming Hwang-Shih-DeCani (γ) γ=-4 γ=-2 γ=1

Results Overall Type 1 error is well-controlled at 2.5% under different information fraction in Phase 3 portion.

Results Overall Type 1 error is well-controlled at 2.5% under different alpha spending function in the Ph3.

The overall Type 1 error is Conclusion Varying alpha splitting and correlation between dual primary endpoint; Varying information fraction and alpha spending function in the group sequential testing; Varying correlation between dual-primary endpoints within group sequential testing; The overall Type 1 error is well-controlled

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Method Extension 1&2: Group Sequential Test for dual- primary hypotheses/endpoints in the Phase 3 portion Assume: X, Y, and Z are test statistics for same endpoints, while S is different. Test statistic Analysis X expansion from Phase 2 to Phase 3 Y primary analysis of the Phase 2 portion Z1 and S1 interim analyses for dual-primary in the Phase 3 portion Z2 and S2 final analyses for dual-primary in the Phase 3 portion

Method Assign α=2.5%(one-sided) to either Phase 2 or 3. Allocate α1 = 0.5% for one of the dual-primary testing and α2 = 2.0% for the other testing as the initial setting in Phase 3 portion. Under the different scenarios, calculate 𝐓𝐲𝐩𝐞 𝐈 𝐞𝐫𝐫𝐨𝐫, under H0 (assume True HR =1 for Y and =(1, 1) for Z and S), derived by Find a simple way to present Pr (X<𝑐, reject H0 for Y in Ph2) + Pr (X ≥𝑐, reject H0 for Z1 or for S1 at interim of Ph3) Pr (X ≥𝑐, Not reject H0 for Z1 and S1 at interim of Ph3, But reject H0 for Z2 or for S2 at final of Ph3) +

Method Evaluate the correlations Z and S ρZ1S1=ρZ2S2 ρZ1S2=ρZ2S1 SC1 0.2 0.1 SC2 0.05 SC3 0.07 0.03 X Z1 Z2 S1 S2 X Z1 Z2 S1 S2

Results Overall Type 1 error is well-controlled under 2.5% under different correlation between Z and S in Phase 3 portion.