A simple mathematical model is proposed that predicts the dynamics of chronic progressive renal disease. The model consists of coupled linear differential equations formed from three state variables, four control parameters, and three parameters related to initial conditions. All have straightforwar
Modelling disease progression in terms of exposure history
β Scribed by Karen Bandeen-Roche; Charles B. Hall; Walter F. Stewart; Scott L. Zeger
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 229 KB
- Volume
- 18
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
β¦ Synopsis
We consider the relationship between accumulating exposure to a putative agent and the associated change in physiologic function. This type of problem is common to prospective studies of cognitive, pulmonary and cardiovascular function. A general model is proposed for data from prospective, observational studies with concurrent measures of exposures and continuous outcome measures. This model permits non-linearity in the relationship between exposure and outcome and is designed to describe outcome in terms of one's entire exposure history. As exposure data are often severely right-skewed, we use regression spline estimation methods which localize the in#uence of extreme points. We illustrate our methodology using data from a longitudinal epidemiologic investigation of the e!ects of amateur boxing on neuropsychologic function.
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