Regression modeling of ordinal data with nonzero baselines

Research output: Contribution to journalArticlepeer-review

Abstract

This paper develops regression models for ordinal data with nonzero control response probabilities. The models are especially useful in dose- response studies where the spontaneous or natural response rate is nonnegligible and the dosage is logarithmic. These models generalize Abbott's formula, which has been commonly used to model binary data with nonzero background observations. We describe a biologically plausible latent structure and develop an EM algorithm for fitting the models. The EM algorithm can be implemented using standard software for ordinal regression. A toxicology data set where the proposed model fits the data but a more conventional model fails is used to illustrate the methodology.

Original languageEnglish (US)
Pages (from-to)308-316
Number of pages9
JournalBiometrics
Volume55
Issue number1
DOIs
StatePublished - Mar 1999

Keywords

  • EM-algorithm
  • Latent structure
  • Nonzero baseline model
  • Ordinal data
  • 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

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