A comparison of different methods to handle missing data in the context of propensity score analysis

• Published in: European journal of epidemiology Vol. 34; no. 1; pp. 23 - 36
• Main Authors: Choi, Jungyeon, Dekkers, Olaf M., le Cessie, Saskia
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Science + Business Media 01.01.2019; Springer Netherlands; Springer Nature B.V
• Subjects: Cardiology; Cohort Studies; Computer Simulation; Confounding Factors (Epidemiology); Data Interpretation, Statistical; Data structures; Epidemiology; Humans; Infectious Diseases; Medicine; Medicine & Public Health; METHODS; Missing data; Models, Statistical; Observational studies; Oncology; Propensity Score; Public Health; Statistical analysis; Statistics as Topic - methods; Effect modification; Missing data; Propensity score analysis; Missingness graph; Multiple imputation; Missing indicator
• DOI: 10.1007/s10654-018-0447-z
• ISSN: 0393-2990; 1573-7284

Propensity score analysis is a popular method to control for confounding in observational studies. A challenge in propensity methods is missing values in confounders. Several strategies for handling missing values exist, but guidance in choosing the best method is needed. In this simulation study, we compared four strategies of handling missing covariate values in propensity matching and propensity weighting. These methods include: complete case analysis, missing indicator method, multiple imputation and combining multiple imputation and missing indicator method. Concurrently, we aimed to provide guidance in choosing the optimal strategy. Simulated scenarios varied regarding missing mechanism, presence of effect modification or unmeasured confounding. Additionally, we demonstrated how missingness graphs help clarifying the missing structure. When no effect modification existed, complete case analysis yielded valid causal treatment effects even when data were missing not at random. In some situations, complete case analysis was also able to partially correct for unmeasured confounding. Multiple imputation worked well if the data were missing (completely) at random, and if the imputation model was correctly specified. In the presence of effect modification, more complex imputation models than default options of commonly used statistical software were required. Multiple imputation may fail when data are missing not at random. Here, combining multiple imputation and the missing indicator method reduced the bias as the missing indicator variable can be a proxy for unobserved confounding. The optimal way to handle missing values in covariates of propensity score models depends on the missing data structure and the presence of effect modification. When effect modification is present, default settings of imputation methods may yield biased results even if data are missing at random.