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

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 methodological work, we explore how these methods perform for three real-world case studies. In all three examples, we use individual patient-level data collected through the University of Michigan Health System and various external population-based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age-related macular degeneration with gold standard external estimates. In case study (c), we evaluate these methods for modeling the association of COVID-19 outcomes and potential risk factors. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR-based research.