A Stagewise Prognostic Control Predictive Approach (SPCPA) for Subgroup Identification and Its Application in a Phase II Study

• Activity Number: 476 - SPEED: Clinical Trial Design, Longitudinal Analysis, and Other Topics in Biopharmaceutical Statistics
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Wanying Li* and Wangshu Zhang and Lovely Goyal and Yuanyuan Xiao
• Companies: Gilead Sciences and Gilead Sciences and Gilead Sciences and Gilead Sciences
• Keywords: subgroup identification; machine learning; predictive factor; prognostic factor; biomarker

Abstract

The statistical problems underlying subgroup identification in clinical studies are to identify the predictors related to the treatment outcome and to determine an algorithm that defines the subgroup of interest based on those predictors. While there are recent advancements in subgroup identification using machine learning techniques, many of them focus on identifying the factors that demonstrate differential predictive values between treatment groups (i.e. predictive factors). However, prognostic factors, which show similar predictive values across treatment groups, can sometimes be of interest. Incorporating them into the subgroup identification procedures potentially can not only improve model performance, but also provide results with more solid clinical and biological interpretations. This paper proposes a stage-wise approach that unifies prognostic and predictive factor identification and subgroup determination with controlled type I error, and presents an example of its application to a phase II study.