Polychotomous multivariate models for coronary heart disease simulation. III. Model sensitivities and risk factor interventions.
This is the third in a series of papers dealing with models of coronary heart disease. Sensitivity analyses of the logistic risk function and the Neyman risk function are reported. The resulting response surfaces are also used to investigate the optimality of the set of values for the risk coefficients. It is shown that the coefficients estimated by maximum likelihood are preferable to the sets from an optimisation procedure. Two different sets of risk coefficients estimated using short periods and entire epochs for the logistic risk function are shown to lead to similar conclusions concerning simulated primary intervention strategies. However, the corresponding risk factor reductions using the Neyman risk function lead to somewhat different effects. Additional information is needed to distinguish between these two assumptions of the risk function used to model coronary heart disease. This underscores the need to understand the effects of the underlying risk function assumed when interpreting simulated outcomes of intervention strategies.
Micropopulation Simulation Resource, University of Minnesota, Minneapolis 55455.
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Zhuo
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Ackerman
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Gatewood
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Kottke
D003198:Computer Simulation; D003327:Coronary Disease / Q000209:etiology*; D006801:Humans; D016015:Logistic Models; D008955:Models, Cardiovascular; D015233:Models, Statistical; D015999:Multivariate Analysis; D012044:Regression Analysis; D012307:Risk Factors; D012680:Sensitivity and Specificity