Multiple imputation methods for bivariate outcomes in cluster randomised trials.

ABSTRACT: Missing observations are common in cluster randomised trials. The problem is exacerbated when modelling bivariate outcomes jointly, as the proportion of complete cases is often considerably smaller than the proportion having either of the outcomes fully observed. Approaches taken to handling such missing data include the following: complete case analysis, single-level multiple imputation that ignores the clustering, multiple imputation with a fixed effect for each cluster and multilevel multiple imputation. We contrasted the alternative approaches to handling missing data in a cost-effectiveness analysis that uses data from a cluster randomised trial to evaluate an exercise intervention for care home residents. We then conducted a simulation study to assess the performance of these approac

SUBMITTER: DiazOrdaz K 

PROVIDER: S-EPMC4981911 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature