2021 journal article

CONCORDANCE AND VALUE INFORMATION CRITERIA FOR OPTIMAL TREATMENT DECISION

ANNALS OF STATISTICS, 49(1), 49–75.

By: C. Shi*, R. Song* & W. Lu*

author keywords: Concordance and value information criteria; optimal treatment regime; tuning parameter selection; variable selection
Source: Web Of Science
Added: March 22, 2021

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.