Practical Considerations for Subgroups Quantification, Selection and Adaptive Enrichment in Confirmatory Trials

Abstract

Subgroup analyses (e.g., baseline information, biomarkers measurements) are commonly encountered and conducted in confirmatory clinical trials to ensure the risk–benefit consistency and appropriate interpretation of the study results. However, there are natural methodological complications that come with multiple analyses which can result in incorrect scientific or regulatory conclusions for subgroup analysis. Typical issues that may arise include, but are not limited to (a) How to make sure subgroup results are reliable and reproducible? (b) How to quantify subgroup reversal effects which may be due to chance finding, or a lack of power in subgroup and/or treatment interaction tests? (c) How to design efficient trials to establish treatment effect in subgroups or/and overall population with proper Type I error control? These are challenges statisticians and clinical trialist regularly face in the drug development process. In this article, we discuss and present different approaches corresponding to these general issues of subgroup analysis, and their impacts and implications on the interpretation of clinical trials as well as the innovations and opportunities in the era of precision medicine. We also conduct simulations to illustrate the operating characteristics of different methodological tools and designs, along with further practical recommendations and guidance from a statistical perspective.