Robust Causal Inference for Point Exposures in Electronic Health Record Based Observational Studies

Music City Center 

Missingness in variables that define eligibility criteria is a pervasive challenge in electronic health record (EHR)-based observational studies. It is typically the case that patients with incomplete eligibility information are excluded from analysis without consideration of assumptions that are being made (implicitly), leaving study conclusions subject to potential selection bias. To the best of our knowledge, however, very little work has been done to mitigate this concern, and existing solutions require correct specification of all relevant models for outcome/treatment/imputation to ensure consistent estimation of causal contrasts. In this work, we propose a robust and efficient estimator of the causal average treatment effect on the treated, study eligible population in cohort studies where eligibility defining covariates are missing at random. We demonstrate the use of our method on EHR data from Kaiser Permanente to analyze differences between two common bariatric surgical interventions for long term weight and glycemic outcomes among a cohort of severely obese patients with type II diabetes mellitus.

Missing Data

Causal Inference

Multiply Robust

Influence Functions

Bariatric Surgery

Diabetes 

Biometrics Section