Pseudolikelihood Modeling of Multivariate Outcomes in Developmental Toxicology
基于小鼠发育毒性研究中的乙二醇数据，提出伪似然方法对多变量畸形指标进行建模，以确定基准剂量，避免高计算负担。
Abstract 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.