Log-Linear Models in the Analysis of Disease Prevalence Data from Survival/ Sacrifice Experiments

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Abstract
The problem of analyzing disease prevalence data from survival experiments in which there may also be some serial sacrifice was considered. The assumptions needed for standard analyses are reviewed in the context of a general model recently proposed. This model is then reparametrized in log-linear form, and a generalized EM algorithm is utilized to obtain maximum likelihood estimates of the parameters for a broad class of unsaturated models. Tests based on the relative likelihood are proposed to investigate the effects of treatment, time and the presence of other diseases on the prevalences and lethalities of specific diseases of interest. An example is given, using data from a large experiment to investigate the effects of low-level radiation on laboratory mice. Some directions for future research are indicated.

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