Speaker
Description
Missing covariate data is a significant source of bias in observational studies that use propensity score (PS) analysis to make causal inference. The accuracy of treatment effect estimation is determined not just by how missing data is handled, but also by the method used to calculate propensity scores. A variety of methods for handling missing covariate data in propensity score analyses have been investigated in earlier research. Complete-case analysis and multiple imputation (MI) are frequently used methods. However, the majority of known studies compare traditional MI approaches (e.g., MICE) with logistic regression-based propensity score estimation, leaving unresolved questions concerning the effectiveness of more flexible machine-learning-based algorithms(2). At the same time, new research demonstrates that tree-based models like random forests might enhance propensity score estimation and minimize bias in complicated, nonlinear data structures(3). Nonetheless, few simulation studies have investigated how imputation and PS estimation approaches interact to affect bias, covariate balance, and overlap. In this talk, we present a simulation study comparing the efficacy of three missing-data handling methods with the complete-case analysis (CC), namely MICE, miss Forest, and random-forest imputation with a combination of two propensity-score estimation techniques: logistic regression and random forest. The performance of each combination of missing data method and PS estimation is evaluated using three criteria: (i) bias in the estimated average treatment effect, (ii) standardized mean differences (SMDs) of variables after weighting, and (iii) overlap between treatment and control propensity score distributions.
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