Marginalized models for longitudinal ordinal data with application to quality of life studies.
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.
Lee
Keunbaik
K
Biostatistics Program, School of Public Health, Louisiana State University Health Science Center, New Orleans, LA 70112, USA.
Daniels
Michael J
MJ
D000970:Antineoplastic Agents / Q000494:pharmacology / Q000627:therapeutic use; D015179:Colorectal Neoplasms / Q000188:drug therapy / Q000523:psychology; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D008954:Models, Biological; D010352:Patient Dropouts; D011788:Quality of Life
D000970:Antineoplastic Agents