Multiple imputation with non-additively related variables: Joint-modeling and approximations.

ABSTRACT: This paper investigates multiple imputation methods for regression models with interacting continuous and binary predictors when continuous variable may be missing. Usual implementations for parametric multiple imputation assume a multivariate normal structure for the variables, which is not satisfied for a binary variable nor its interaction with a continuous variable. To accommodate interactions, missing covariates are multiply imputed from conditional distribution in a manner consistent with the joint model. Alternative imputation methods under multivariate normal assumptions are also considered as candidate approximations and evaluated in a simulation study. The results suggest that the joint modeling procedure performs generally well across a wide range of scenarios and so do the appr

SUBMITTER: Kim S 

PROVIDER: S-EPMC6991942 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature