Estimating relative risk functions in case-control studies using a nonparametric logistic regression.
The authors describe an approach to the analysis of case-control studies in which the exposure variables are continuous, i.e., quantitative variables, and one wishes neither to categorize levels of the exposure variable nor to assume a log-linear relation between level of exposure and disease risk.
A dose-response association of an exposure variable with a disease outcome can be depicted by estimated relative risks at various exposure levels, and the functional relation between exposure dose and disease risk is here termed a relative risk function (RRF).
A RRF takes values that are greater than zero : Values less than one imply lower risk ; the value one implies no risk, and values greater than one imply increased risk, when compared with a reference value.
The authors describe how a nonparametric logistic regression can be used to estimate and display these RRFs.
Using data from a previously published case-control study of diet and colon cancer, RRFs for total energy, dietary fiber, and alcohol intakes are compared with the original results obtained from using categorized levels of exposure variables.
For total energy and alcohol intakes, there were meaningful differences in study results based on the two analytic approaches.
For energy, the nonparametric logistic regression detected a significant protective effect of low intakes, which was not found in the original analysis. (...)
Mots-clés Pascal : Régression logistique, Statistique, Epidémiologie, Méthodologie, Risque, Tumeur maligne, Côlon, Alimentation, Modèle loglinéaire, Homme, Etude cas témoin
Mots-clés Pascal anglais : Logistic regression, Statistics, Epidemiology, Methodology, Risk, Malignant tumor, Colon, Feeding, Loglinear model, Human, Case control study
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 96-0439556
Code Inist : 002B30A01A1. Création : 10/04/1997.