A Stagewise Prognostic Control Predictive Approach (SPCPA) for Subgroup Identification with application in a phase II study Wanying Li, Wangshu Zhang, Lovely Goyal, Yuanyuan Xiao Biomarker Biostatistics, Gilead Sciences, Foster City, CA Contact info: Wanying.Li@gilead.com

Fundamental statistical problems underlying “exploratory” subgroup identification prognostic only prognostic and predictive Identify the predictive factors of differential treatment effect Determine an algorithm to define the subgroup (including cutoff determination) predictive only neither prognostic nor predictive Adapted from Fridlyand J 2013

Identify Predictive Factors Stagewise Prognostic Control Predictive Approach (SPCPA) with the advantage of improving signal detection power by adjusting for the prognostic effects Prognostic Factors Placebo patients     Treated patients         Predictive Factors Identify Prognostic Factors, Derive Prognostic Score and Estimate Individual Treatment Effect This approach borrows concepts from counterfactual models, in which there are two possible outcomes for each person (one under each treatment assignment), only one of which can be observed, and it is the difference between the two outcomes that is important. SPCPA first calculates the Individual Treatment Effect (ITE) according to the counterfactual concept in causal inference; it then applies GUIDE/SIDES to identify the subgroup(s) oand predictive factors based on the ITE. Identify Predictive Factors Slide 3 of 7

A Phase II study example N = ~240, randomized 1:2 to placebo:active arms Response rate (RR): ~50% in the active arm, vs ~20% in the placebo arm Similar RR to other competitor drugs Objective: to support ongoing phase III trials Identify prognostic factors Identify a subpopulation that can have further improvement in RR

Multiplicity adjusted p-value A promising subgroup was identified based on a signature of two variables Sample size Prognostic effect adjusted response* P value Multiplicity adjusted p-value Overall Active Placebo Diff Treatment effect Treatment effect improvement Overall population 233 154 79 0.386 0.027 0.359 1.14E-09 Variable 1 ≤ 3rd quartile 176 121 55 0.435 -0.024 0.459 1.45E-11 0.0034 0.108 Variable 1 ≤ 3rd quartile & Variable 2 category 1 141 96 45 0.481 -0.041 0.522 1.99E-12 0.0006 0.021 *After adjustment of placebo and prognostic effect, the expected response rate in the placebo arm should be 0.

Conclusions We proposed a framework that integrates the process of identifying prognostic and predictive factors. This framework can handle most types of the response endpoints except for time-to-event endpoint. This new approach potentially can increases detection power by adjusting for prognostic effects. Its output has type I error assessment and has explicit final algorithm with cutoffs, which can facilitate clinical and biological interpretations. Simulation work on comparing SPCPA with other existing methods is still ongoing.

Acknowledgements References Gilead Biomarker Biostat, Tuan Nguyen, Jacqueline Tarrant, Julie Ma, Neby Bekele References Fridlyand J, Yeh RF, Mackey H, Bengtsson T, Delmar P, Spaniolo G, Lieberman G. 2013 An industry statistician's perspective on PHC drug development. Contemp Clin Trials. 36(2):624-35 Lipkovich I, Dmitrienko A, Denne J, Enas G. 2011. Subgroup identification based on differential effect search (SIDES): a recursive partitioning method for establishing response to treatment in patient subpopulations. Statistics in Medicine; 30:2601–2621. Lipkovich, I. and Dmitrienko, A., 2017. Tutorial in biostatistics: data‐driven subgroup identification and analysis in clinical trials. Statistics in Medicine, 36(1), pp.136-196