Flexible modeling of the effects of serum cholesterol on coronary heart disease mortality.

Abstract

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 < 0.01) and the ratio of total serum cholesterol to high density lipoprotein cholesterol (p < 0.02) significantly better than the parametric logistic regression. Validation studies confirmed that, among new observations arising from the same population, generalized additive model estimates predicted outcomes better than the parametric estimates. Nonlinear effects of both lipid measures were robust and may be clinically important. The authors conclude that the linearity assumption inherent in parametric models may result in biased estimates of the effects of total serum cholesterol on coronary heart disease mortality and recommend that their findings be verified in a nonparametric analysis of data from another large prospective study.