Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding.

ABSTRACT: In this study, we focus on estimating the heterogeneous treatment effect (HTE) for survival outcome. The outcome is subject to censoring and the number of covariates is high-dimensional. We utilize data from both the randomized controlled trial (RCT), considered as the gold standard, and real-world data (RWD), possibly affected by hidden confounding factors. To achieve a more efficient HTE estimate, such integrative analysis requires great insight into the data generation mechanism, particularly the accurate characterization of unmeasured confounding effects/bias. With this aim, we propose a penalized-regression-based integrative approach that allows for the simultaneous estimation of parameters, selection of variables, and identification of the existence of unmeasured confounding effects.

SUBMITTER: Ye X 

PROVIDER: S-EPMC12317910 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature