An Empirical Comparison of Multiple Imputation Approaches for Treating Missing Data in Observational Studies
Missing data are a common and significant problem that researchers and data analysts encounter in applied research. Because most statistical procedures require complete data, missing data can substantially affect the analysis and the interpretation of results if left untreated. Methods to treat missing data have been developed so that missing values are imputed and analyses can be conducted using standard statistical procedures. Among these missing data methods, Multiple Imputation has received considerable attention and its effectiveness has been explored, for example, in the context of survey and longitudinal research. This paper compares four Multiple Imputation approaches for treating missing continuous covariate data under MCAR, MAR, and MNAR assumptions in the context of propensity score analysis and observational studies. The comparison of four Multiple Imputation approaches in terms of bias and variability in parameter estimates, Type I error rates, and statistical power is presented. In addition, complete case analysis (listwise deletion) is presented as the default analysis that would be conducted if missing data are not treated. Issues are discussed, and conclusions and recommendations are provided.