Strategies for Identifying Predictive Biomarkers and Subgroups with Enhanced Treatment Effect in Clinical Trials Using SIDES

Abstract

Several approaches to identification of predictive biomarkers and subgroups of patients with enhanced treatment effect have been proposed in the literature. The SIDES method introduced in Lipkovich et al. (2011) adopts a recursive partitioning algorithm for screening treatment-by-biomarker interactions. This article introduces an improved biomarker discovery/subgroup search method (SIDEScreen). The SIDEScreen method relies on a two-stage procedure that first selects a small number of biomarkers with the highest predictive ability based on an appropriate variable importance score and then identifies subgroups with enhanced treatment effect based on the selected biomarkers. The two-stage approach helps increase the signal-to-noise ratio by screening out noninformative biomarkers. We evaluate operating characteristics of the standard SIDES method and two SIDEScreen procedures based on fixed and adaptive screens. Our main finding is that the adaptive SIDEScreen method is a more flexible biomarker discovery tool than SIDES and it better handles multiplicity in complex subgroup search problems. The methods presented in the article are illustrated using a clinical trial example.