Details

Autor(en) / Beteiligte

• Shi, Chengchun
• Song, Rui
• Lu, Wenbin

Titel

Concordance and value information criteria for optimal treatment decision

Ist Teil von

• The Annals of statistics, 2021-02, Vol.49 (1), p.49

Ort / Verlag

Hayward: Institute of Mathematical Statistics

Erscheinungsjahr

2021

Quelle

Project Euclid Complete

Beschreibungen/Notizen

• 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.