Multi-study R-learner for estimating heterogeneous treatment effects across studies using statistical machine learning.

ABSTRACT: Heterogeneous treatment effect (HTE) refers to the nonrandom, explainable variation in treatment effects for individuals in a population. HTE estimation is central to precision medicine, where accurate effect estimates can inform personalized treatment decisions. In practice, patients can present with covariate profiles that overlap with multiple studies, raising the challenge of optimally informing treatment decisions in a multi-study setting. We proposed a flexible statistical machine learning (ML) framework, the multi-study $ R $-learner, that leverages multiple studies to estimate the HTE. Existing multi-study approaches often assume that study-specific (i) conditional average treatment effect (CATE), (ii) expected potential outcome under no treatment given covariates, and (iii) treatm

SUBMITTER: Shyr C 

PROVIDER: S-EPMC12713001 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature