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An appraisal of methods for the analysis of longitudinal ordinal response data with random dropout using a non-homogeneous Markov model

Research output: Contribution to journalJournal article

Published

Journal publication date2010
JournalCommunications in Statistics – Simulation and Computation
Journal number5
Volume39
Number of pages22
Pages1027-1048
Original languageEnglish

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.