Random effects in censored ordinal regression: Latent structure and Bayesian approach

Minge Xie, Douglas G. Simpson, Raymond J. Carroll

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)376-383
Number of pages8
Issue number2
StatePublished - Jun 2000


  • Censored response
  • Gibbs sampler/Metropolis algorithm
  • Hierarchical Model
  • Risk assessment

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


Dive into the research topics of 'Random effects in censored ordinal regression: Latent structure and Bayesian approach'. Together they form a unique fingerprint.

Cite this