Assessing Treatment Effects in Observational Data with Missing Confounders: A Comparative Study of Practical Doubly-Robust and Traditional Missing Data Methods

For safety and rare outcome studies in pharmacoepidemiology, multiple, large databases are often merged to improve statistical power and create a more generalizable cohort; however, in many settings detailed confounder data will only be available on a subset of individuals.This webinar covered a discussion on two practical-to-implement, doubly robust estimators for this setting, one relying on a type of survey calibration, and another utilizing targeted maximum likelihood estimation (TMLE) and compared their performance with that of more traditional missing data methods in a detailed numerical study. Numerical work included plasmode simulation studies that emulate the complex data structure of a real large electronic health records cohort in order to compare anti-depressant therapies in a setting where a key confounder is prone to missingness.