Sequential ordinal modeling with applications to survival data

James H. Albert; Siddhartha Chib

doi:10.1111/j.0006-341X.2001.00829.x

Sequential ordinal modeling with applications to survival data

James H. Albert
, Siddhartha Chib

Research output: Contribution to journal › Article › peer-review

79 Scopus citations

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.

Original language	English
Pages (from-to)	829-836
Number of pages	8
Journal	Biometrics
Volume	57
Issue number	3
DOIs	https://doi.org/10.1111/j.0006-341X.2001.00829.x
State	Published - 2001

Keywords

Bayes factor
Cumulative ordinal probit and logit model
Discrete hazard function
Gibbs sampling
Marginal likelihood
Metropolis-Hastings algorithm
Model comparison
Nonnested models
Sequential ordinal probit and logit
Training sample prior

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10.1111/j.0006-341X.2001.00829.x

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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.",

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AB - 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.

KW - Bayes factor

KW - Cumulative ordinal probit and logit model

KW - Discrete hazard function

KW - Gibbs sampling

KW - Marginal likelihood

KW - Metropolis-Hastings algorithm

KW - Model comparison

KW - Nonnested models

KW - Sequential ordinal probit and logit

KW - Training sample prior

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VL - 57

SP - 829

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JO - Biometrics

JF - Biometrics

IS - 3

ER -

Sequential ordinal modeling with applications to survival data

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Keywords

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