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Missing data arise in most applied settings and are ubiquitous in electronichealth records (EHR). When data are missing not at random (MNAR) with respectto measured covariates, sensitivity analyses are often considered. Thesepost-hoc solutions, however, are often unsatisfying in that they are notguaranteed to yield concrete conclusions. Motivated by an EHR-based study oflong-term outcomes following bariatric surgery, we consider the use of doublesampling as a means to mitigate MNAR outcome data when the statistical goalsare estimation and inference regarding causal effects. We describe assumptionsthat are sufficient for the identification of the joint distribution ofconfounders, treatment, and outcome under this design. Additionally, we deriveefficient and robust estimators of the average causal treatment effect under anonparametric model and under a model assuming outcomes were, in fact,initially missing at random (MAR). We compare these in simulations to anapproach that adaptively estimates based on evidence of violation of the MARassumption. Finally, we also show that the proposed double sampling design canbe extended to handle arbitrary coarsening mechanisms, and derive nonparametricefficient estimators of any smooth full data functional.

Double sampling for informatively missing data in electronic health record-based comparative effectiveness research