Exploratory Subgroup Identification for Biopharmaceutical Development

Abstract

A major challenge in developing precision medicines is the identification and confirmation of patient subgroups where an investigational regimen has a positive benefit–risk balance. In biopharmaceutical development, exploring these patient subgroups of potential interest is usually achieved by constructing decision rules (a signature) using single or multiple biomarkers in a data-driven fashion, accompanied by rigorous statistical performance evaluation to account for potential overfitting issues inherent in subgroup searching. This chapter provides a comprehensive review of general considerations in exploratory subgroup analysis, investigates popular statistical learning algorithms for biomarker signature development, and proposes statistical principles for subgroup performance assessment. An example of subgroup identification for an immunology disease treatment leading to regulatory label inclusion will be provided.