Evaluating Unmeasured Confounding Factors in Claims Data Using Linked Electronic Health Records: A Proof‐of‐Principle Analysis

Claims-based analyses can suffer from residual and unmeasured confounding due to factors that are poorly captured in claims. Some of these factors may be measured in other data sources, such as in structured fields of electronic health records (EHR), for example, laboratory test results, or in free-text physician notes. We conducted a proof-of-principle study to demonstrate a process for evaluating the potential risk of confounding-factors poorly captured in claims data but measurable in the EHR as part of drug safety surveillance activities. In future practical applications, this approach could be used along with other sensitivity analyses to evaluate potential residual confounding (e.g., E-values, negative controls). We used claims-EHR linked data from the Mass General Brigham site of the US Food and Drug Administration's (FDA) Sentinel Real World Evidence Data Enterprise. We extracted a cohort that was previously used in a prototypical Sentinel claims-based query that compared initiators of sacubitril-valsartan vs. angiotensin-converting enzyme inhibitors or angiotensin receptor blockers on the risk of angioedema. In this cohort, we used EHR data to characterize angioedema risk factors poorly captured in claims and observed that claims-based proxies balanced most risk factors that were measurable only in EHR data. While quantitative bias analysis methods can be used to adjust for residual confounding using external information on magnitude and direction of bias, this was deemed unnecessary for this example due to the observed balance achieved on risk factors for angioedema measured in the EHR. A robust linked EHR-claims data infrastructure is crucial for routine application of these methods to evaluate and mitigate residual confounding in drug safety surveillance studies.