An Appraisal of Methods for the Analysis of Longitudinal Ordinal Response Data with Random Dropout Using a Nonhomogeneous Markov Model

Abstract

There are many methods for analyzing longitudinal ordinal response data with random dropout. These include maximum likelihood (ML), weighted estimating equations (WEEs), and multiple imputations (MI). In this article, using a Markov model where the effect of previous response on the current response is investigated as an ordinal variable, the likelihood is partitioned to simplify the use of existing software. Simulated data, generated to present a three-period longitudinal study with random dropout, are used to compare performance of ML, WEE, and MI methods in terms of standardized bias and coverage probabilities. These estimation methods are applied to a real medical data set.

Keywords:

• Multiple imputation
• Nonhomogeneous Markov model
• Random dropout
• Short-period longitudinal data
• Weighted estimating equations

Mathematics Subject Classification:

• 62P10

Notes

Bold numbers are estimated parameters with standardized bias >0.4.

Bold numbers are estimated parameters with standardized bias >0.4.

Bold numbers are estimated parameters with standardized bias >0.4.

Bold numbers are coverage probabilities less than 0.90.

Bold numbers are significant at 5% level.