Bias reduction and inference for electronic health record data under selection and phenotype misclassification: three case studies.

ABSTRACT: Electronic Health Records (EHR) are not designed for population-based research, but they provide access to longitudinal health information for many 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. Recently, 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 to the met

SUBMITTER: Beesley LJ 

PROVIDER: S-EPMC7781342 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature