1216Combining propensity score-weighting and multiple imputation is not a trivial task

Abstract

Abstract Background Propensity score (PS)-weighting and multiple imputation are two widely used statistical methods. Combining the two is not trivial and has received little attention in theory and practice. We present our considerations for their combination with application to a study of long-distance migration and post-traumatic stress disorder. We elaborate on the assumptions underlying the methods and discuss the methodological and practical implications of our choices and alternatives. Methods We made a number of choices a priori: to use logistic regression-based PS to produce “standardised mortality ratio”-weights and SMC-FCS to multiply impute missing data. We present a methodology to combine the methods by choosing the PS model based on covariate balance, using this model as the substantive model in the multiple imputation, producing and averaging the point estimates from each multiply imputed data set to give the estimate of association and computing the percentile confidence interval by bootstrapping. Results In our application, a simple PS model was chosen as the substantive model for imputing 10 data sets with 40 iterations and repeating the entirety 999 times to obtain a bootstrap confidence interval. Computing time was approximately 36 hours. Conclusions Our structured approach is demanding in both work-load and computational time. We do not consider the prior a draw-back: it makes some of the underlying assumptions explicit and the latter may be a nuisance that diminishes with time. Key messages Combining propensity score-weighting and multiple imputation is not a trivial task.