title: A class of markov models for longitudinal ordinal data.
abstract: Generalized linear models with serial dependence are often used for short longitudinal series. Heagerty (2002, Biometrics58, 342-351) has proposed marginalized transition models for the analysis of longitudinal binary data. In this article, we extend this work to accommodate longitudinal ordinal data. Fisher-scoring algorithms are developed for estimation. Methods are illustrated on quality-of-life data from a recent colorectal cancer clinical trial.
authors: Department of Statistics, University of Florida, Gainesville, Florida 32611, USA.
authors: Keunbaik
authors: K
authors: Lee
authors: Michael J
authors: MJ
authors: Daniels
mesh_terms: D001699:Biometry / Q000379:methods; D015179:Colorectal Neoplasms / Q000453:epidemiology* / Q000628:therapy*; D003627:Data Interpretation, Statistical; D005221:Fatigue / Q000175:diagnosis* / Q000453:epidemiology*; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D011379:Prognosis; D011788:Quality of Life; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D016896:Treatment Outcome