Work

A Class of Markov Models for Longitudinal Ordinal Data

Year: 2007

Type: article

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.

Source: Biometrics

DOI: 10.1111/j.1541-0420.2007.00800.x

Authors: Keunbaik Lee, Michael J. Daniels

Institution: University of Florida

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Mayo Clinic, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute

Awards: G2900563331, G359255199, G3961503985, G834453155, Analyzing Complex Longitudinal Data in Behavior Sciences