Subgroup Identification via Multiple Change Point Detection: Methods and Applications

Subgroup identification is a significant research area in statistics and machine learning, aiming to partition a heterogeneous population into more homogeneous subgroups to enable precise inference and personalized decision‐making. Among the various tools available, change point analysis has emerged as a powerful approach for detecting structural changes in data sequences, and it plays an increasingly important role in subgroup identification. In this paper, we provide a systematic review of recent advances in subgroup identification methods based on efficient multiple change point detection methods: (a) We first review the two‐step multiple change point detection method (TSMCD) and its application from linear regression to survival analysis. (b) We then discuss the construction of the threshold variable, including the recent developments in the change plane regression model and the change surface regression model. This review aims to provide researchers with a comprehensive perspective to promote the further application and development of change point analysis in subgroup identification and precision medicine.