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

TL;DR

This paper proposes a doubly robust statistical framework, JAIPW, to correct outcome-dependent selection bias in multi-cohort EHR studies, improving estimation accuracy over traditional methods especially under model misspecification.

Contribution

The paper introduces the JAIPW method, which combines data from multiple cohorts and external samples with a double robustness property to address outcome-dependent selection bias.

Findings

JAIPW reduces bias and RMSE significantly compared to existing methods.

Simulation studies demonstrate up to six times lower bias with JAIPW.

Application to MGI data yields estimates aligned with national benchmarks.

Abstract

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.