Pseudolikelihood modeling of multivariate outcomes in developmental toxicology

• 成果类型: Article
• 署名作者: Geys, H; Molenberghs, G; Ryan, LM
• 署名单位: Hasselt University; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2669986
• 发表日期: 1999
• 页码: 734-745
• 关键词: clustered binary data; sample survey data; logistic-regression; likelihood; toxicity; teratology; glycol; tests; mice

摘要

The primary goal of this article is to determine benchmark doses based on the ethylene glycol study, which comprises data from a developmental toxicity study in mice. Because the data involve a vector of malformation indicators, a flexible model for multivariate clustered data is required. An exponential family model is considered and pseudolikelihood-based inferential tools are proposed, hence avoiding excessive computational requirements.