Multiple imputation by predictive mean matching in cluster-randomized trials.

ABSTRACT:

Background

Random effects regression imputation has been recommended for multiple imputation (MI) in cluster randomized trials (CRTs) because it is congenial to analyses that use random effects regression. This method relies heavily on model assumptions and may not be robust to misspecification of the imputation model. MI by predictive mean matching (PMM) is a semiparametric alternative, but current software for multilevel data relies on imputation models that ignore clustering or use fixed effects for clusters. When used directly for imputation, these two models result in underestimation (ignoring clustering) or overestimation (fixed effects for clusters) of variance estimates.

Methods

We develop MI procedures based on PMM that leverage these opposing estimated biases in th

SUBMITTER: Bailey BE 

PROVIDER: S-EPMC7106802 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature