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February 2021 Concordance and value information criteria for optimal treatment decision
Chengchun Shi, Rui Song, Wenbin Lu
Ann. Statist. 49(1): 49-75 (February 2021). DOI: 10.1214/19-AOS1908

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.

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Chengchun Shi. Rui Song. Wenbin Lu. "Concordance and value information criteria for optimal treatment decision." Ann. Statist. 49 (1) 49 - 75, February 2021. https://doi.org/10.1214/19-AOS1908

Information

Received: 1 March 2017; Revised: 1 August 2019; Published: February 2021
First available in Project Euclid: 29 January 2021

Digital Object Identifier: 10.1214/19-AOS1908

Subjects:
Primary: 62E99

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.49 • No. 1 • February 2021
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