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

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, w...