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Marginalized models for longitudinal ordinal data with application to quality of life studies
Author(s) -
Lee Keunbaik,
Daniels Michael J.
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3352
Subject(s) - categorical variable , dropout (neural networks) , random effects model , ordinal regression , statistics , computer science , econometrics , ordinal data , marginal model , missing data , mathematics , regression analysis , machine learning , medicine , meta analysis
Random effects are often used in generalized linear models to explain the serial dependence for longitudinal categorical data. Marginalized random effects models (MREMs) for the analysis of longitudinal binary data have been proposed to permit likelihood‐based estimation of marginal regression parameters. In this paper, we propose a model to extend the MREM to accommodate longitudinal ordinal data. Maximum marginal likelihood estimation is proposed utilizing quasi‐Newton algorithms with Monte Carlo integration of the random effects. Our approach is applied to analyze the quality of life data from a recent colorectal cancer clinical trial. Dropout occurs at a high rate and is often due to tumor progression or death. To deal with events due to progression/death, we used a mixture model for the joint distribution of longitudinal measures and progression/death times and use principal stratification to draw causal inferences about survivors. Copyright © 2008 John Wiley & Sons, Ltd.