The validity of propensity score analysis using complete cases with partially observed covariates

Excerpt

In their simulation study, Choi et al. [1] compared different methods for handling missing covariate data in the context of propensity score analysis. This study addressed an important question on how propensity score matching and weighting perform coupled with different methods handing missing data when a treatment effect is homogenous or heterogeneous. Their study concluded that if a treatment effect is homogenous then a complete case analysis yields a valid average treatment effect estimate under missing at random (MAR) and even under missing not at random (MNAR) mechanisms. We are concerned that the authors made this conclusion from a limited range of simulation scenarios for a homogeneous treatment effect that considered only a null treatment effect and missing mechanisms independent of the outcome. …