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

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.

KEYWORDS:

• Chain equation
• Correlated ordinal
• Multiple imputation
• Data, Multivariate probit model
• MCMC

Funding

Dr. Belin's effort was supported in part by NIH grants UL1-TR000124, P30-MH58017, and P50-HL105188.