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A Class of Markov Models for Longitudinal Ordinal Data
Author(s) -
Lee Keunbaik,
Daniels Michael J.
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00800.x
Subject(s) - ordinal data , markov chain , biometrics , longitudinal data , class (philosophy) , ordinal regression , computer science , statistics , econometrics , mathematics , artificial intelligence , data mining
Summary Generalized linear models with serial dependence are often used for short longitudinal series. Heagerty (2002, Biometrics 58, 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.