Résumé :
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[BDSP. Notice produite par INIST PFHN2R0x. Diffusion soumise à autorisation]. Current understanding of the impact of lipids and other risk factors on coronary heart disease is largely based on the results of parametric multiple regression analyses of large prospective studies. To assess the potential impact of the a priori assumption of linearity of continuous risk factors on the results of parametric analyses, the authors completed a secondary analysis of the Lipid Research Clinics Prevalence and Follow-up Studies (1972-1987) data using an assumption-free nonparametric modeling approach. The effects of total serum cholesterol and the ratio of total serum cholesterol to high density lipoprotein cholesterol, adjusted for common risk factors, were estimated using a smoothing spline method available in the generalized additive model extension of the multiple logistic regression. The data set included 2,512 men in the random sample of the Lipid Research Clinics study who did not take lipid-lowering medications. During the median follow-up of 12.6 years, 94 coronary heart disease deaths occurred. The generalized additive model fits the effects of total serum cholesterol (p
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