Using conditional logistic regression to fit proportional odds models to interval censored data

Daniel Rabinowitz, Rebecca A. Betensky, Anastasios A. Tsiatis

Research output: Contribution to journalArticle

Abstract

An easily implemented approach to fitting the proportional odds regression model to interval-censored data is presented. The approach is based on using conditional logistic regression routines in standard statistical packages. Using conditional logistic regression allows the practitioner to sidestep complications that attend estimation of the baseline odds ratio function. The approach is applicable both for interval-censored data in settings in which examinations continue regardless of whether the event of interest has occurred and for current status data. The methodology is illustrated through an application to data from an AIDS study of the effect of treatment with ZDV+ddC versus ZDV alone on 50% drop in CD4 cell count from baseline level. Simulations are presented to assess the accuracy of the procedure.

Original languageEnglish (US)
Pages (from-to)511-518
Number of pages8
JournalBiometrics
Volume56
Issue number2
DOIs
StatePublished - Jun 2000

Keywords

  • AIDS
  • Current status data
  • Nonparametric maximum likelihood
  • Survival analysis

ASJC Scopus subject areas

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

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