TY - JOUR
T1 - A comparison of approaches to implementing propensity score methods following multiple imputation
AU - Penning De Vries, Bas B.L.
AU - Groenwold, Rolf H.H.
PY - 2017
Y1 - 2017
N2 - Background: In observational research on causal effects, missing data and confounding are very common problems. Multiple imputation and propensity score methods have gained increasing interest as methods to deal with these, but despite their popularity methodologists have mainly focused on how they perform in isolation. Methods: We studied two approaches to implementing propensity score methods following multiple imputation, both of which have been used in applied research, and compared their performance by way of Monte Carlo simulation for a continuous outcome and partially unobserved covariate, treatment or outcome data. In the first, so-called Within, approach, propensity score analysis is performed within each of m imputed datasets, and the resulting m effect estimates are averaged. In the Across approach, for each subject the m estimated propensity scores are averaged first, after which the propensity score method is implemented based on each subject’s average propensity score. Because of its common use, complete case analysis was also implemented. Five propensity score estimators were studied, including regression, matching, and inverse probability weighting. Results: The Within approach was found to be superior to the Across approach in terms of bias as well as variance in settings with missing covariate data, when missing data were missing at random as well as when they were missing completely at random. In settings with incomplete treatment or outcome values only, the Within and Across approaches yielded similar results. Complete case analysis was generally least efficient and unbiased only in scenarios where missing data were missing completely at random. Conclusion: We advise researchers not to use the Across approach as the default method, because even when data are missing completely at random, this may yield biased effect estimates. Instead, the Within is the preferred approach when implementing propensity score methods following multiple imputation.
AB - Background: In observational research on causal effects, missing data and confounding are very common problems. Multiple imputation and propensity score methods have gained increasing interest as methods to deal with these, but despite their popularity methodologists have mainly focused on how they perform in isolation. Methods: We studied two approaches to implementing propensity score methods following multiple imputation, both of which have been used in applied research, and compared their performance by way of Monte Carlo simulation for a continuous outcome and partially unobserved covariate, treatment or outcome data. In the first, so-called Within, approach, propensity score analysis is performed within each of m imputed datasets, and the resulting m effect estimates are averaged. In the Across approach, for each subject the m estimated propensity scores are averaged first, after which the propensity score method is implemented based on each subject’s average propensity score. Because of its common use, complete case analysis was also implemented. Five propensity score estimators were studied, including regression, matching, and inverse probability weighting. Results: The Within approach was found to be superior to the Across approach in terms of bias as well as variance in settings with missing covariate data, when missing data were missing at random as well as when they were missing completely at random. In settings with incomplete treatment or outcome values only, the Within and Across approaches yielded similar results. Complete case analysis was generally least efficient and unbiased only in scenarios where missing data were missing completely at random. Conclusion: We advise researchers not to use the Across approach as the default method, because even when data are missing completely at random, this may yield biased effect estimates. Instead, the Within is the preferred approach when implementing propensity score methods following multiple imputation.
KW - Causal effects
KW - Confounding
KW - Missing data
KW - Multiple imputation
KW - Propensity scores
UR - http://www.scopus.com/inward/record.url?scp=85039559897&partnerID=8YFLogxK
U2 - 10.2427/12630
DO - 10.2427/12630
M3 - Article
AN - SCOPUS:85039559897
VL - 14
SP - e12630-1-e12630-21
JO - Epidemiology Biostatistics and Public Health
JF - Epidemiology Biostatistics and Public Health
IS - 4
ER -