Estimating adjusted risk differences by multiply-imputing missing control binary potential outcomes following propensity score-matching.

ABSTRACT: We describe a new method to combine propensity-score matching with regression adjustment in treatment-control studies when outcomes are binary by multiply imputing potential outcomes under control for the matched treated subjects. This enables the estimation of clinically meaningful measures of effect such as the risk difference. We used Monte Carlo simulation to explore the effect of the number of imputed potential outcomes under control for the matched treated subjects on inferences about the risk difference. We found that imputing potential outcomes under control (either single imputation or multiple imputation) resulted in a substantial reduction in bias compared with what was achieved using conventional nearest neighbor matching alone. Increasing the number of imputed potential outcom

SUBMITTER: Austin PC 

PROVIDER: S-EPMC8596520 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature