(untitled) (Epidemiology, Biostatistics, and Public Health, 2022)
Background: Missing values are a common problem for data analyses in observational studies, which are frequently applied in health services research. This paper examines the usefulness of different approaches to tackle the problem of incomplete observational data, focusing whether the Multiple Imputation (MI) strategy yields adequate estimates when applied to a complex analysis framework. Methods: Based on observational study data originally comparing three forms of psychotherapy, a simulation study with different missing data scenarios was conducted. The considered analysis model comprised a propensity score-adjusted treatment effect estimation. Missing values were handled by complete case analysis, different MI approaches, as well as mean and regression imputation. Results: All point estimators of the applied methods lay within the 95% confidence interval of the treatment effect derived from the complete simulation data set. Highest deviation was observed for complete case analysis. A distinct superiority of MI methods could not be demonstrated. Conclusion: Since there was no clear benefit of one method to deal with missing values over another, health services researchers faced with incomplete observational data are well-advised to apply different imputation methods and compare the results in order to get an impression of their sensitivity.