Quantile-Based Subgroup Identification for Randomized Clinical Trials

Youngjoo Cho () and Debashis Ghosh
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Youngjoo Cho: The University of Texas at El Paso
Debashis Ghosh: Colorado School of Public Health

Statistics in Biosciences, 2021, vol. 13, issue 1, No 6, 90-128

Abstract: 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.

Keywords: Heterogeneous treatment effects; Imputation; LASSO; Missing data; Quantile regression (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12561-020-09286-z

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