Multiple imputation by predictive mean matching in cluster-randomized trials

Tuning multiple imputation by predictive mean matching and local residual draws

BACKGROUND Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor wit...

Author ' s response to reviews Title : Tuning multiple imputation by predictive mean matching and local residual

Imputation strategies for missing binary outcomes in cluster randomized trials

BACKGROUND Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized. Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate depen...

Multiple imputation methods for bivariate outcomes in cluster randomised trials

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 i...

Title: Imputation Strategies for Missing Binary Outcomes in Cluster Randomized Trials

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