Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification.

ABSTRACT: Electronic health records (EHR) are not designed for population-based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow-up

SUBMITTER: Beesley LJ 

PROVIDER: S-EPMC9826451 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature