A multiple imputation method for incomplete correlated ordinal data using multivariate probit models. Issue 3 (16th March 2017)

• Record Type: Journal Article
• Authors: Zhang, Xiao; Li, Quanlin; Cropsey, Karen; Yang, Xiaowei; Zhang, Kui; Belin, Thomas
• Is Part Of: Communications in statistics. Volume 46:Issue 3(2017)
• Page Start: 2360
• Page End: 2375
• Publication Date: 2017-03-16
• Subjects: Chain equation -- Correlated ordinal -- Multiple imputation -- Data, Multivariate probit model -- MCMC; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1043388
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: 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.