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利用共轭广义线性混合模型分析分组数据AI 解读

Summary This article concerns a class of generalized linear mixed models for two-level grouped data, where the random effects are uniquely indexed by groups and are independent. We derive necessary and sufficient conditions for the marginal likelihood to be expressed in explicit form. These models are unified under the conjugate generalized linear mixed models framework, where conjugate refers to the fact that the marginal likelihood can be expressed in closed form, rather…

贝叶斯模型平均及其在饮用水砷暴露基准剂量估计中的应用AI 解读

An important component of quantitative risk assessment involves characterizing the dose-response relationship between an environmental exposure and adverse health outcome and then computing a benchmark dose, or the exposure level that yields a suitably low risk. This task is often complicated by model choice considerations, because risk estimates depend on the model parameters. We propose using Bayesian methods to address the problem of model selection and derive a…

用于诊断相关结果线性模型正态误差的 Cholesky 残差AI 解读

Despite the widespread popularity of linear models for correlated outcomes (e.g., linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this article we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated marginal residual vector by the Cholesky decomposition of the inverse of the estimated marginal variance…

致畸研究中多结局的交叉随机效应模型AI 解读

Human teratogens often manifest themselves through a broad spectrum of adverse effects. Although often not serious when considered individually, such outcomes taken together may represent a syndrome that can lead to serious developmental problems. Accordingly, studies that investigate the effect of human teratogens on fetal development typically record the presence or absence of a multitude of abnormalities, resulting in the data of multivariate binary form for each infant.…