Multi-study R -learner for estimating heterogeneous treatment effects across studies using statistical machine learning
Year: 2024
Type: article
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) treatment assignment mechanism are identical across studies, but these assumptions may not hold in practice due to differences in study populations, protocols, or designs. To this end, we developed our framework to directly account for these three types of between-study heterogeneity. It builds upon recent advances in cross-study learning and uses a data-adaptive objective function to combine cross-study estimates of nuisance functions with study-specific CATEs via membership probabilities, which enable information to be borrowed across studies. The multi-study $ R $-learner extends the $ R $-learner to the multi-study setting and is flexible in its ability to incorporate ML techniques. In the series estimation framework, we showed that the proposed method is asymptotically normal and more efficient than the $ R $-learner when there is between-study heterogeneity in the treatment assignment mechanisms. We illustrated using cancer data from randomized controlled trials and observational studies that the multi-study $ R $-learner performs favorably in the presence of between-study heterogeneity.
Source: Biostatistics
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,461
Awards: Advancing Statistical Methods for Multi-Study Predictions, Training Grant in Quantitative Sciences for Cancer Research, Development and implementation of statistical machine learning methods to shorten rare disease odysseys, Development and implementation of statistical machine learning methods to shorten rare disease odysseys
OpenAlex ID: W4417498771