Polychotomous multivariate models for coronary heart disease simulation. IV. The impact of physiological aging

Zhangqing Zhuo, Ya Jung Tsai, Eugene Ackerman, Lael C Gatewood

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4 Scopus citations


This is an extension of a series of papers dealing with certain models used in the simulation of coronary heart disease. The current study investigates implications of including age as a risk factor in the models discussed in the preceding papers. The effects of using age as a risk factor were investigated in two ways. In one of these, age is interpreted as age of entry into the study; it is similar to the other risk factors in that it is assumed to be constant throughout the study. In the other, age is interpreted as the actual age; thus it increases during the course of simulations. Two polychotomous, multivariate risk functions developed in previous studies, the logistic risk and the Neyman exponential risk, were used to explore the effects of including age as a risk factor. The estimated risk coefficient for age was found to be statistically significant for both functions. The model performance was evaluated by comparing the observational data with outcomes simulated using Monte Carlo techniques. It was found that the logistic risk function failed to describe the observations either with age as a constant or with aging during the simulations. The models including the Neyman exponential risk avoidance fit the data well. The evaluation of the results indicates that aging during the simulations is better than using only the age as the constant value at entry to the study.

Original languageEnglish (US)
Pages (from-to)287-296
Number of pages10
JournalInternational Journal of Bio-Medical Computing
Issue number3
StatePublished - 1994

Bibliographical note

Funding Information:
This work was supported in part by NIH Grant P41-RR01632. The North Karelia dataset was provided by Dr. Jukka Salonen of the University of Kuopio, Finland. The efforts of Jan Marie Lundgren in the preparation of the final manuscript are acknowledged.


  • Aging
  • Computer simulation
  • Coronary disease
  • Exponential risk avoidance models
  • Logistic models


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