Conference on Simulation in the Medical Sciences
Proceedings of the 1997 Western MultiConference
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Using Constrained Categorical Regression to Identify Structural Relationships in Epidemiological Data
- RMG Consulting, 2000 Fresno Road, Plano, Texas 75074
- Martingale Research Corporation
- UT Southwestern Medical Center at Dallas, 8267 Elmbrook, Suite 250, Dallas, TX 75247-9141
- Key Words
- Statistical Analysis, Categorical, Epidemiological, Modeling, Regression, Neural Network
- Improved methods for confirmation of structural relationships between demographic information sources and medical and psychiatric conditions are invaluable to local, county, state, and national groups and agencies. A neural network based nonlinear regression model known as the CCR (Constrained Categorical Regression) model is introduced that provides an explicit mechanism for representing structural relationships as logical rules (i.e., Boolean functions) whose respective contributions to predicting explicit outcome probabilities are weighted by the model's free parameters. The CCR modeling approach combines in a novel way the classical generalized logit modeling methodology with relatively new statistical hypothesis testing techniques designed for solving problems in econometric and artificial neural network modeling. In this paper, the relationship of the CCR model to classical methods of statistical inference is discussed and the results obtained from applying the CCR model to the analysis of a particular epidemiological data set are presented.
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NOTE: This material was based on work sponsored by the National Institute on Alcohol Abuse and Alcoholism. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Institute on Alcohol Abuse and Alcoholism.
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