Incorporation of missing indicator with multiple imputation in propensity score analysis with partially observed...

One of the primary challenges encountered in propensity score (PS) weighting is the presence of observations with missing covariates. In such cases, several potential solutions based on multiple imputation have been proposed. The most prevalent of these is the MI_te method, which combines treatment effect estimates derived from imputed datasets. A limited number of PS studies have incorporated the MI_te method with the missing indicator method; however, these studies only incorporated the missing indicator into the PS model. The aim of this simulation study is to propose two novel methods that incorporate the missing indicator approach with the MI_te. This incorporation either entails including the missing indicator into the outcome model (MIMI_o) or, alternatively, into both the outcome and PS model (MIMI_pso). The construction of the simulation scenarios was predicated on three elements: the mechanism of missing data, the type of treatment effect, and the presence of unmeasured confounding. In the presence of unmeasured confounding, the MIMI_pso method was the most effective method under the MAR mechanism. In the context of the MNAR mechanism, the method that exhibited the lowest bias was MIMI_o for homogeneous treatment effect and MIMI_pso for heterogeneous treatment effect. The MI_te method exhibited the highest levels of bias and variation. In view of the difficulties involved in identifying the mechanism of missing data, the variability in treatment effects across subgroups and the potential for unmeasured confounding variables in practice, researchers are encouraged to utilize the MIMI_pso method.