Polychotomous multivariate models for coronary heart disease simulation III. Model sensitivities and risk factor interventions

Zhangqing Zhuo, Eugene Ackerman, Laël Gatewood, Thomas Kottke

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)205-220
Number of pages16
JournalInternational Journal of Bio-Medical Computing
Issue number3
StatePublished - 1991

Bibliographical note

Funding Information:
This work was supported in part by NIH Grant P41-RRO1632. The North Karelia dataset was provided by Dr. Jukka Salonen of the University of Kuopio, Finland. This paper includes information and descriptions of simulations which form part of the Ph.D. thesis of the first author. The efforts of Jan Marie Lundgren in the preparation of the final manuscript are acknowledged.


  • Computer simulation
  • Coronary disease
  • Exponential risk avoidance models
  • Intervention studies
  • Logistic models
  • Sensitivity studies


Dive into the research topics of 'Polychotomous multivariate models for coronary heart disease simulation III. Model sensitivities and risk factor interventions'. Together they form a unique fingerprint.

Cite this