Random effects in censored ordinal regression: latent structure and Bayesian approach.

abstract

• 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.

authors

• Carroll, Raymond

published proceedings

• Biometrics

author list (cited authors)

• Xie, M., Simpson, D. G., & Carroll, R. J.

citation count

• 16

complete list of authors

• Xie, M||Simpson, DG||Carroll, RJ

publication date

• June 2000

publisher

• Wiley  Publisher

published in

• Biometrics  Journal

keywords

• Animals
• Bayes Theorem
• Biometry
• Humans
• Mice
• Random Allocation
• Rats
• Regression Analysis
• Tetrachloroethylene
• Toxicology

PubMed Central ID

• 10877292

Digital Object Identifier (DOI)

• 10.1111/j.0006-341x.2000.00376.x

start page

• 376

end page

• 383

volume

• 56

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1111%2Fj.0006-341x.2000.00376.x