Simulated scenarios varied regarding missing mechanism, presence of effect modification or unmeasured confounding. Concurrently, we aimed to provide guidance in choosing the optimal strategy.
These methods include: complete case analysis, missing indicator method, multiple imputation and combining multiple imputation and missing indicator method. In this simulation study, we compared four strategies of handling missing covariate values in propensity matching and propensity weighting. Several strategies for handling missing values exist, but guidance in choosing the best method is needed.
A challenge in propensity methods is missing values in confounders. Propensity score analysis is a popular method to control for confounding in observational studies.
0 kommentar(er)
