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Concordance and value information criteria for optimal treatment decision

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  • Shi, Chengchun
  • Song, R
  • Lu, W

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 fixedp 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.

Suggested Citation

  • Shi, Chengchun & Song, R & Lu, W, 2021. "Concordance and value information criteria for optimal treatment decision," LSE Research Online Documents on Economics 102105, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:102105
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    File URL: http://eprints.lse.ac.uk/102105/
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    Cited by:

    1. Gao, Yuhe & Shi, Chengchun & Song, Rui, 2023. "Deep spectral Q-learning with application to mobile health," LSE Research Online Documents on Economics 119445, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    concordance and value information criteria; optimal treatment regime; tuning parameter selection; variable selection;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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