Quantitative Issues in Cancer Research Working Seminar

Luke Benz
Doctoral Student, Department of Biostatistics, Harvard University

“A simulation study to compare causal inference methods for point exposures with missing confounders”

ABSTRACT: Causal inference methods based on electronic health record (EHR) databases must simultaneously handle confounding and missing data. Vast work exists to address these two separately, but surprisingly few papers attempt to address them simultaneously. In practice, when faced with simultaneous missing data and confounding, analysts may proceed by first imputing missing data and subsequently use outcome regression or inverse-probability weighting (IPW) to address confounding. However, little is known about the performance of such ad-hoc methods. In a recent paper Levis et al. (2022) outline a robust framework for tackling these problems together and introduce a pair of semi-parametric efficient estimators for the average treatment effect (ATE) which differ in the conditions regarding missing data they assume. In this work we present a series of simulations, motivated by a published EHR based study of the long-term effects of bariatric surgery on weight outcomes, to investigate the new estimators of Levis et al., and compare them to existing ad-hoc methods. While the latter perform well in certain scenarios, no single estimator is uniformly best. As such, the work of Levis et al. may serve as a reasonable default for causal inference when handling confounding and missing data together.