In many epidemiologic data, the dose-response relation between a continuous exposure and the risk of disease abruptly changes when the exposure variable reaches an unknown threshold level, the so-called change-point.
Although several methods are available for dose-response assessment with dichotomous outcomes, none of them provide inferential procedures to estimate change-points.
In this paper, we describe a two-segmented logistic regression model, in which the linear term associated with a continuous exposure in standard logistic regression is replaced by a two-segmented polynomial function with unknown change-point, which is also estimated.
A modified, iteratively reweighted least squares algorithm is presented to obtain parameter estimates and confidence intervals, and the performance of this model is explored through simulation.
Finally, a two-segmented logistic regression model is applied to a case-control study of the association of alcohol intake with the risk of myocardial infarction and compared with alternative analyses.
The ability of two-segmented logistic regression to estimate and provide inferences for the location of change-points and for the magnitude of other parameters of effect will make this model a useful complement to other methods of dose-response analysis in epidemiologic studies.
Am J Epidemiol 1998 ; 148 : 631-42.
Mots-clés Pascal : Exposition, Facteur risque, Relation dose réponse, Régression logistique, Point changement, Epidémiologie, Méthodologie, Modèle mathématique, Analyse statistique, Homme
Mots-clés Pascal anglais : Exposure, Risk factor, Dose activity relation, Logistic regression, Change point, Epidemiology, Methodology, Mathematical model, Statistical analysis, Human
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Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0510823
Code Inist : 002B30A01A1. Création : 23/03/1999.