Statistical methods for developmental toxicity. Analysis of clustered multivariate binary data

Publication Type:
Journal Article
Annals of the New York Academy of Sciences, 1999, 895 pp. 196 - 211
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012000405OK.pdf660.1 kB
Adobe PDF
This paper discusses some of the statistical issues that arise from developmental toxicity studies, wherein pregnant mice are exposed to chemicals in order to assess possible adverse effects on developing fetuses. We begin with a review of some current approaches to risk assessment, based on NOAELs, and provide justification for the use of methods based on dose-response models. Due to the hierarchical nature of the data, such models are more complicated in the present context than, say, in cancer studies. For example, multivariate binary outcomes arise when each fetus in a litter is assessed for the presence of malformations and/or low birth weight. We describe a multivariate exponential family model that works well for these data and that is flexible in terms of allowing response rates to depend on cluster size. Maximum likelihood estimation of model parameters and the construction of score tests for dose effect are briefly discussed. Results are illustrated with data from several NTP studies.
Please use this identifier to cite or link to this item: