Transition models for change-point estimation in logistic regression

Roberto Pastor-Barriuso, Eliseo Guallar, Josef Coresh

Research output: Contribution to journalReview articlepeer-review

52 Scopus citations


Although a wide variety of change-point models are available for continuous outcomes, few models are available for dichotomous outcomes. This paper introduces transition methods for logistic regression models in which the dose-response relationship follows two different straight lines, which may intersect or may present a jump at an unknown change-point. In these models, the logit includes a differentiable transition function that provides parametric control of the sharpness of the transition at the change-point, allowing for abrupt changes or more gradual transitions between the two different linear trends, as well as for estimation of the location of the change-point. Linear-linear logistic models are particular cases of the proposed transition models. We present a modified iteratively reweighted least squares algorithm to estimate model parameters, and we provide inference procedures including a test for the existence of the change-point. These transition models are explored in a simulation study, and they are used to evaluate the existence of a change-point in the association between plasma glucose after an oral glucose tolerance test and mortality using data from the Mortality Follow-up of the Second National Health and Nutrition Examination Survey.

Original languageEnglish (US)
Pages (from-to)1141-1162
Number of pages22
JournalStatistics in Medicine
Issue number7
StatePublished - Apr 15 2003


  • Change-point
  • Hypothesis test
  • Logistics regression
  • Segmented regression models
  • Statistical inference
  • Transition models

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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