Considerations for using multiple imputation in propensity score-weighted analysis

Abstract

We present our considerations for using multiple imputation to account for missing data in propensity score-weighted analysis with bootstrap percentile confidence interval. We outline the assumptions underlying each of the methods and discuss the methodological and practical implications of our choices and briefly point to alternatives. We made a number of choices a priori for example to use logistic regression-based propensity scores to produce standardized mortality ratio-weights and Substantive Model Compatible-Full Conditional Specification to multiply impute missing data (given no violation of underlying assumptions). We present a methodology to combine these methods by choosing the propensity score model based on covariate balance, using this model as the substantive model in the multiple imputation, producing and averaging the point estimates from each multiple imputed data set to give the estimate of association and computing the percentile confidence interval by bootstrapping. The described methodology is demanding in both work-load and in computational time, however, 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 will diminish with faster computers and better implementations.

The association between long-distance migration and PTSD prevalence in Syrian refugees

“…In the following we give a brief summary of the applied statistical methodology and refer to Supplementary Text 2, Supplementary Tables 1-2 and Supplementary Figs. 3, 4, 5 for further details as well as [41] for an in-depth discussion of its implications and usage. Missing data was multiply imputed after verifying for each partly observed variable that the data was "everywhere missing at random" (see Supplementary Text 2 and [42].…”