A Doubly Robust Framework for Addressing Outcome-Dependent Selection Bias in Multi-Cohort EHR Studies

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to six times lower relative bias and five times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates closely aligned with national benchmark estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure variable is not measured in the external probability sample.

💡 Research Summary

This paper tackles the pervasive problem of selection bias in electronic health record (EHR) studies that draw participants from multiple clinics or centers, each with its own recruitment mechanism. When the disease outcome itself influences the probability of being selected—a situation the authors call “outcome‑dependent selection”—standard inverse‑probability‑weighting (IPW) methods can produce severely biased estimates, especially if the selection model is misspecified. The authors therefore develop a novel Joint Augmented Inverse Probability Weighted (JAIPW) estimator that combines data from several non‑probability EHR cohorts with an external probability sample (e.g., NHANES) and achieves double robustness.

The methodological development proceeds in several steps. First, the authors formalize the target population as a finite sample from an infinite super‑population and define cohort‑specific selection indicators (S_k) and selection probabilities (\pi_k(X_k)=P(S_k=1\mid X_k)), where (X_k) includes the disease indicator (D), covariates that affect both disease and selection ((Z_{2k})), and additional selection‑only variables ((W_k)). Assuming independence of selection across cohorts conditional on covariates (Condition C1.3), the overall probability of being in at least one cohort is (\pi(X_{\text{mult}})=1-\prod_{k}