Addressing common sources of bias in studies of new-onset type 2 diabetes following COVID that use electronic health record data

Abstract

Observational studies based on cohorts built from electronic health records (EHR) form the backbone of our current understanding of the risk of new-onset diabetes following COVID. EHR-based research is a powerful tool for medical research but is subject to multiple sources of bias. In this viewpoint, we define key sources of bias that threaten the validity of EHR-based research on this topic (namely misclassification, selection, surveillance, immortal time, and confounding biases), describe their implications, and suggest best practices to avoid them in the context of COVID-diabetes research.