Statistical Methods in Disease Risk Assessment and Prediction

在这个项目下，我们继续开发一种方法来处理环境混合物研究中的检测限。处理受LOD影响的问题的传统方法，包括全案例分析、替代方法和协变量分布的参数化建模，是可行的，但可能导致效率损失或偏差。我们考虑了受LOD影响的多个相关协变量的多元加速失效时间模型和结果的广义线性模型。提出了一种基于半参数伪似然的两阶段方法来估计环境混合物对健康结果的影响。我们用LIFECODES出生队列数据说明了该方法的实际效用，在该数据中，我们将该方法与现有方法进行了比较，以分析与孕妇氧化应激相关的多种尿微量金属。

Under this project, we continued to develop an approach to handle assay limit of detection in environmental mixture studies. Conventional approaches to deal with coveriates subject to LOD, including complete-case analysis, substitution methods and parametric modeling of the covariate distribution, are feasible but may result in efficiency loss or bias. We considered a multivariate accelerated failure time model for the multiple correlated covariates subject to LOD and a generalized linear model for the outcome. A two-stage procedure based on semiparametric pseudo-likelihood is proposed for estimating the effects of environmental mixtures on the health outcome. We illustrate the practical utility of the methodology with the LIFECODES birth cohort data, where we compare our approach to existing approaches in analysis of multiple urinary trace metals in association with oxidative stress in pregnant women.