Latent class based multiple imputation approach for missing categorical data.

ABSTRACT: In this paper we propose a latent class based multiple imputation approach for analyzing missing categorical covariate data in a highly stratified data model. In this approach, we impute the missing data assuming a latent class imputation model and we use likelihood methods to analyze the imputed data. Via extensive simulations, we study its statistical properties and make comparisons with complete case analysis, multiple imputation, saturated log-linear multiple imputation and the Expectation- Maximization approach under seven missing data mechanisms (including missing completely at random, missing at random and not missing at random). These methods are compared with respect to bias, asymptotic standard error, type I error, and 95% coverage probabilities of parameter estimates. Simulation

SUBMITTER: Gebregziabher M 

PROVIDER: S-EPMC6290917 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature