This paper focuses on the analysis of clustered multivariate binary data that arise from developmental toxicity studies. In these studies, pregnant mice are exposed to chemicals to assess possible adverse eects on developing fetuses. Multivariate binary outcomes arise when each fetus in a litter is
A multivariate logistic model (MLM) for analyzing binary family data
โ Scribed by Karunaratne, P. Mahinda; Elston, Robert C.
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 26 KB
- Volume
- 76
- Category
- Article
- ISSN
- 0148-7299
No coin nor oath required. For personal study only.
โฆ Synopsis
We consider modeling the familial correlation between 2 related individuals using a multiple logistic regressive model. It is shown that there is a discrepancy in the marginal probability of the second individual. We investigate the conditions under which this discrepancy can be minimized and show how it can have a direct effect on handling missing values and ascertainment. We derive a functional relationship between the parameters in the model that eliminates this discrepancy, hence solving the problems that can arise in the handling of missing values and ascertainment. Because this methodology fails when there are more than 2 related individuals, we present a new model based on a multivariate logistic distribution. Residual familial correlations can be directly related to the parameters of this model. The likelihood for family data under this model is independent of the order in which the family members enter the calculation. The marginal probabilities can be easily computed. Am.
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We compare mixed effects logistic regression models for binary response data with two nested levels of clustering. The comparison of these models occurs in the context of developmental toxicity data sets, for which multiple types of outcomes (first level) are measured on each rat pup (second level)