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Transition models for change-point estimation in logistic regression.

Roberto Pastor-Barriuso·Eliseo Guallar·Josef Coresh
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Abstract

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.

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AdultAgedAlgorithmsComputer SimulationDiabetes MellitusDose-Response Relationship, DrugGlucose Tolerance TestHumansLogistic ModelsMiddle AgedModels, StatisticalJournal ArticleResearch Support, Non-U.S. Gov't

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BibTeXRIS

Roberto Pastor-Barriuso, Eliseo Guallar, Josef Coresh. 2003-04-15. Transition models for change-point estimation in logistic regression.. https://doi.org/10.1002/sim.1045

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