Multiple imputation for discrete data: Evaluation of the joint latent normal model.

ABSTRACT: Missing data are ubiquitous in clinical and social research, and multiple imputation (MI) is increasingly the methodology of choice for practitioners. Two principal strategies for imputation have been proposed in the literature: joint modelling multiple imputation (JM-MI) and full conditional specification multiple imputation (FCS-MI). While JM-MI is arguably a preferable approach, because it involves specification of an explicit imputation model, FCS-MI is pragmatically appealing, because of its flexibility in handling different types of variables. JM-MI has developed from the multivariate normal model, and latent normal variables have been proposed as a natural way to extend this model to handle categorical variables. In this article, we evaluate the latent normal model through an extens

SUBMITTER: Quartagno M 

PROVIDER: S-EPMC6618333 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature