Stochastic compartmental modeling techniques have been employed to simulate coronary heart disease morbidity and mortality. In the current paper, polychotomous logistic models are used to describe the relationship between risk of disease and multiple risk factors, effect modification and confounding variables. The process of estimating the parameters for two risk factors and three types of outcomes is described for a population followed for five years. A Statistical Analysis System (SAS) procedure was used to estimate risk factor coefficients based on two partial periods and on the entire five year epoch. Most of the estimated coefficients were found to be statistically significant. The model performance was evaluated by comparing the observational data with simulated outcomes using a micropopulation and Monte Carlo techniques. Two different tests of goodness of fit were used. Satisfactory fits were obtained both for the risk coefficients based on two partial periods and those based on the entire epoch. This indicates that the model is suitable for simulation of the effects of intervention strategies. The use of the entire epoch involved estimates of one half as many parameters as did the use of two partial periods. Accordingly, it is concluded that only the entire epoch need be considered for future studies of this population.
Bibliographical noteFunding Information:
This work was supported in part by NIH Grants P41-RR01632 and ROl-HL23727. Data was provided by Dr. Ancel Keys and the Division of Epidemiology, University of Minnesota. This paper extends and updates concepts included in Hyeoun-Ae Park’s Ph.D. thesis . The efforts of Jan Marie Lundgren in the preparation of the final manuscript are acknowledged.
- Biological models
- Computer simulation
- Coronary disease
- Logistic models
- Risk factors