Subgroup analysis of heterogeneous groups is a crucial step in the development of individualized treatment and personalized marketing strategies.Regression-based approaches are one of the main schools of subgroup analysis,a paradigm that divides predictor variables into two parts with heterogeneous and homogeneous effects and divides the sample into subgroups based on the heterogeneous effects.However,most of the existing regression-based subgroup analysis methods have two major limitations:First,they still consider the sample homogeneous within subgroups and do not fully consider individual effects;Second,the common contamination phenomenon of homogeneous effect variables is not taken into account,which will lead to large bias in the model results.To address these challenges,we propose a robust individualized subgroup analysis.We use a multidirectional separation penalty function to achieve individualized effects analysis for the heterogeneous part of the model and use y-divergence to obtain robust estimates for the contaminated homogeneous part.We also propose an efficient alternating iterative two-step algorithm,combining coordinate descent and alternating direction method of multipliers(ADMM)techniques to implement this process.Our proposed method is further illustrated by simulation studies and analysis of a skin cutaneous melanoma dataset.
万方数据
维普
