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Quantile-Based Subgroup Identification for Randomized Clinical Trials

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  • Youngjoo Cho

    (The University of Texas at El Paso)

  • Debashis Ghosh

    (Colorado School of Public Health)

Abstract

In many clinical trials, treatment effects may be heterogeneous across subgroups so that individuals in such groups receive different benefits. Recognizing this difference can be quite important for the purposes of clinical decision-making. For personalized medicine problems, we argue in this paper that it is natural to consider quantiles. Subgroup identification methods have not been developed for quantiles. In this article, we introduce approaches to quantile-based subgroup identification that are motivated by potential outcomes considerations. A variety of penalized regression approaches are considered in this paper. The causal framework highlights the utility of the approach in terms of incorporation of heterogeneity. Simulated datasets as well as an example from the AIDS clinical trial are used to illustrate the methodology.

Suggested Citation

  • Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    • Handle: RePEc:spr:stabio:v:13:y:2021:i:1:d:10.1007_s12561-020-09286-z
    DOI: 10.1007/s12561-020-09286-z
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    References listed on IDEAS

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    Cited by:

    1. Cho, Youngjoo & Zhan, Xiang & Ghosh, Debashis, 2022. "Nonlinear predictive directions in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

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