Bias correction models for electronic health records data in the presence of non-random sampling.

ABSTRACT: Electronic health records (EHRs) contain rich clinical information for millions of patients and are increasingly used for public health research. However, non-random inclusion of subjects in EHRs can result in selection bias, with factors such as demographics, socioeconomic status, healthcare referral patterns, and underlying health status playing a role. While this issue has been well documented, little work has been done to develop or apply bias-correction methods, often due to the fact that most of these factors are unavailable in EHRs. To address this gap, we propose a series of Heckman type bias correction methods by incorporating social determinants of health selection covariates to model the EHR non-random sampling probability. Through simulations under various settings, we demonstr

SUBMITTER: Kim J 

PROVIDER: S-EPMC10941326 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature