Quantitative Issues in Cancer Research Working Seminar

Luke Benz
PhD Student, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Adjusting for Selection Bias Due to Missing Eligibility Criteria in Emulated Target Trials

Abstract: Target trial emulation (TTE) is a popular framework for observational studies based on electronic health records (EHR). A key component of this framework is determining the patient population eligible for inclusion in the study. Missingness in variables that define eligibility criteria, however, presents a major challenge, yet in practice, patients with incomplete data are frequently excluded from analysis despite the possibility of selection bias, which can arise when subjects with observed eligibility data are fundamentally different than excluded subjects. Despite this, to the best of our knowledge, however, very little work has developed methods to mitigate this concern. In this work, we propose a novel conceptual framework to address selection bias in a TTE studies, tailored towards time-to-event endpoints, and describe estimation and inferential procedures via inverse probability weighting (IPW). Under a realistic simulation infrastructure, which adequately captures the unique intricacies of EHR data, we characterize common settings under which missing eligibility data poses the threat of selection bias and evaluate the ability of IPW to address it. Finally, we demonstrate use of our method to evaluate the effect of bariatric surgery on microvascular outcomes.