Polychotomous multivariate models for coronary heart disease simulation. II. Comparisons of risk functions

This is the second in a series of papers dealing with models of coronary heart disease. Three different types of statistical models are considered as risk functions: the multivariate logistic model, the Cox proportional hazard model and the Neyman exponential risk avoidance model. The types of models differ in the form hypothesized for the probability of occurrence of coronary heart disease outcomes: incident myocardial infarct, cardiac death, and death from other causes. Although the three risk functions are strikingly different, they can all be tested using the CRISPERS chronic disease simulation system. Simulations were performed using data from North Karelia, Finland. The polychotomous multivariate logistic risk function is convenient for studies involving increasing numbers of risk factors. The Cox proportional hazard regression model is shown to be unsuitable for the cohort dataset used as well as for some of the intended uses of the simulation models. The Neyman exponential risk avoidance model involves time in a quite different fashion. It has the inherent advantage of being easier to relate to underlying biological mechanisms because it is the integral of first order rate equations. It is concluded that more than one risk function should be evaluated for simulations of coronary heart disease.