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

Ghahroodi, ZR and Ganjali, M and Navvabpour, H and Berridge, D (2010) An appraisal of methods for the analysis of longitudinal ordinal response data with random dropout using a nonhomogeneous Markov model. Communications in Statistics - Simulations and Computing, 39 (5). pp. 1027-1048. ISSN 0361-0918

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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.

Item Type:Article
Uncontrolled Keywords:Multiple imputation, nonhomogeneous Markov model, random output, short-period longitudinal data, weighted estimating
Subjects:5. Quantitative Data Handling and Data Analysis > 5.7 Longitudinal Data Analysis
ID Code:1740
Deposited By: L-W-S user
Deposited On:31 Mar 2011 07:04
Last Modified:09 Feb 2012 15:03

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