title: Sequential ordinal modeling with applications to survival data.
abstract: This paper considers the class of sequential ordinal models in relation to other models for ordinal response data. Markov chain Monte Carlo (MCMC) algorithms, based on the approach of Albert and Chib (1993, Journal of the American Statistical Association 88, 669-679), are developed for the fitting of these models. The ideas and methods are illustrated in detail with a real data example on the length of hospital stay for patients undergoing heart surgery. A notable aspect of this analysis is the comparison, based on marginal likelihoods and training sample priors, of several nonnested models, such as the sequential model, the cumulative ordinal model, and Weibull and log-logistic models.
authors.affiliation: Department of Mathematics and Statistics, Bowling Green State University, Ohio 43403, USA. albert@bgnet.bgsu.edu
authors.forename: J H
authors.initials: JH
authors.lastname: Albert
authors.forename: S
authors.initials: S
authors.lastname: Chib
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D006801:Humans; D007902:Length of Stay; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016019:Survival Analysis; D013903:Thoracic Surgery / Q000706:statistics & numerical data