A multiple imputation method for incomplete correlated ordinal data using multivariate probit models

Academic Article

Abstract

  • © 2017 Taylor & Francis Group, LLC. The multiple imputation technique has proven to be a useful tool in missing data analysis. We propose a Markov chain Monte Carlo method to conduct multiple imputation for incomplete correlated ordinal data using the multivariate probit model. We conduct a thorough simulation study to compare the performance of our proposed method with two available imputation methods–multivariate normal-based and chain equation methods for various missing data scenarios. For illustration, we present an application using the data from the smoking cessation treatment study for low-income community corrections smokers.
  • Digital Object Identifier (doi)

    Author List

  • Zhang X; Li Q; Cropsey K; Yang X; Zhang K; Belin T
  • Start Page

  • 2360
  • End Page

  • 2375
  • Volume

  • 46
  • Issue

  • 3