Simulation of micropopulations in epidemiology: tutorial 2. Analytic forms of event probabilities. A series of tutorials illustrated by coronary heart disease models

Simulation of micropopulations in epidemiology: tutorial. 4. Evaluations of simulation models. A series of tutorials illustrated by coronary heart disease models

The first tutorial in this series (Ackerman E: Simulation of micropopulations in epidemiology: Tutorial 1. Simulation: an introduction, Int J Biomed Comput, 36 (1994) 229-238) introduces the general approach of simulation of micropopulation models of coronary disease using Monte Carlo techniques. The modeling process includes the selection of functional forms to represent the probability of state transfers. Alternative functional forms are described in the second tutorial (Ackerman E: Simulation of micropopulations in epidemiology: Tutorial 2. Analytic forms of event probabilities, Int J Biomed Comput, 37 (1994) 139-149). The third tutorial (Ackerman E: Simulation of micropopulations in epidemiology: Tutorial 3. Simulation model evaluation methods, Int J Biomed Comput, 37 (1994) 195-204) considers the actual risk factors used in the models and the compartmentalization of the population. However, that tutorial emphasizes the methods of evaluating different models of coronary heart disease. In the current tutorial, the evaluation methods are applied to the models previously introduced. Essential to this is the concept of degrees of freedom. Limitations of that concept are discussed with specific reference to models of coronary heart disease. The estimation of optimal values for the risk coefficients is considered further. The various tests of appropriateness are applied to specific models and the role of sensitivity analysis is further illustrated. Discussions of intervention strategies and their simulation is deferred to the following tutorial.

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

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.

Simulation of micropopulations in epidemiology: tutorial. 3. Simulation model evaluation methods. A series of tutorials illustrated by coronary heart disease models

This is the third in a series of tutorials concerning the simulation of micropopulation models to support epidemiological research. The series emphasizes techniques used in studies at the National Micropopulation Simulation Resource at the University of Minnesota. For pedagogic purposes, applications to coronary heart disease (CHD) models are used to illustrate the principles and methodologies employed. All of the models presented are implemented using available software. A variety of tests and techniques are used to evaluate these models. This tutorial presents some of those methods stressing the advantages and limitations of the tests rather than the formal definitions. To make the evaluation methods more understandable, some of the risk factors for CHD models are introduced. The interpretation of the evaluation depends critically on the goals of the modeling effort. The subset of evaluation methods presented includes investigations of the epidemiological, mathematical and statistical aspects of the models.