title: Statistical methods for developmental toxicity. Analysis of clustered multivariate binary data.
abstract: 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 side. 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.
authors.affiliation: Harvard School of Public Health, Boston, MA, USA. ryan@jimmy.harvard.edu
authors.forename: L
authors.initials: L
authors.lastname: Ryan
authors.forename: G
authors.initials: G
authors.lastname: Molenberghs
mesh_terms: D000818:Animals; D005314:Embryonic and Fetal Development / Q000187:drug effects*; D005260:Female; D008432:Mathematical Computing; D051379:Mice; D008962:Models, Theoretical; D011247:Pregnancy; D011297:Prenatal Exposure Delayed Effects; D018570:Risk Assessment; D013723:Teratogens / Q000633:toxicity*; D018675:Toxicity Tests
chemical_list: D013723:Teratogens