Concordance and value information criteria for optimal treatment decision
Chengchun Shi, Rui Song, Wenbin Lu
The Annals of Statistics · 2021 · Volume 49 · Issue 1
Abstract
Personalized medicine is a medical procedure that receives considerable scientific and commercial attention. The goal of personalized medicine is to assign the optimal treatment regime for each individual patient, according to his/her personal prognostic information. When there are a large number of pretreatment variables, it is crucial to identify those important variables that are necessary for treatment decision making. In this paper, we study two information criteria: the concordance and value information criteria, for variable selection in optimal treatment decision making. We consider both fixed-$p$ and high dimensional settings, and show our information criteria are consistent in model/tuning parameter selection. We further apply our information criteria to four estimation approaches, including robust learning, concordance-assisted learning, penalized A-learning and sparse concordance-assisted learning, and demonstrate the empirical performance of our methods by simulations.
Keywords
62E99, Concordance and value information criteria, Optimal treatment regime, tuning parameter selection, Variable selection
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