Nonlinear predictive directions in clinical trials

Youngjoo Cho, Xiang Zhan and Debashis Ghosh

Computational Statistics & Data Analysis, 2022, vol. 174, issue C

Abstract: In many clinical trials, individuals in different subgroups may experience differential treatment effects. This leads to the need to consider individualized differences in treatment benefit. The general concept of predictive directions, which are risk scores motivated by potential outcomes considerations, is introduced. These techniques borrow heavily from the literature from sufficient dimension reduction (SDR) and causal inference. Initially directions assuming an idealized complete data structure are formulated. Then a new connection between SDR and kernel machine methodology for detection of treatment-covariate interactions is developed. Simulation studies and a real data analysis from AIDS Clinical Trials Group (ACTG) 175 data show the utility of the proposed approach.

Keywords: Causal effect; Heterogeneity of treatment effect; Machine learning; Kernel methods; Personalized medicine (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:174:y:2022:i:c:s0167947322000561

DOI: 10.1016/j.csda.2022.107476

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