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.
Original language | English (US) |
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Pages (from-to) | 1141-1162 |
Number of pages | 22 |
Journal | Statistics in Medicine |
Volume | 22 |
Issue number | 7 |
DOIs | |
State | Published - Apr 15 2003 |
Keywords
- Change-point
- Hypothesis test
- Logistics regression
- Segmented regression models
- Statistical inference
- Transition models
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability