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Random Effects in Censored Ordinal Regression: Latent Structure and Bayesian Approach
Author(s) -
Xie Minge,
Simpson Douglas G.,
Carroll Raymond J.
Publication year - 2000
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.0006-341x.2000.00376.x
Subject(s) - ordinal regression , ordinal data , gibbs sampling , statistics , econometrics , bayesian probability , regression analysis , mathematics , computer science
Summary. This paper discusses random effects in censored ordinal regression and presents a Gibbs sampling approach to fit the regression model. A latent structure and its corresponding Bayesian formulation are introduced to effectively deal with heterogeneous and censored ordinal observations. This work is motivated by the need to analyze interval‐censored ordinal data from multiple studies in toxicological risk assessment. Application of our methodology to the data offers further support to the conclusions developed earlier using GEE methods yet provides additional insight into the uncertainty levels of the risk estimates.