Statistical Methods in Disease Risk Assessment and Prediction

今年有两项重大成就。 首先，我们开发了一个非参数和半参数回归方法的双变量故障时间的结果。这是因为许多生物医学研究跟踪参与者的多种相关健康结果。同时对这些结果进行建模可能比单个模型更有效，并允许我们描述发展多种疾病的风险，以便于在个人有其他疾病史的情况下进行风险预测。除了两篇描述这些方法的期刊手稿外，我们还出版了Chapman & Hall关于这个主题的书。 其次，我们开发了一种方法来处理检测限的药物混合物的研究。传统的方法来处理覆盖受LOD，包括完整的情况下分析，替代方法和协变量分布的参数建模，是可行的，但可能会导致效率损失或偏差。我们考虑了多个相关协变量的多变量加速失效时间模型， 结果的线性模型。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用性，在此我们将我们的方法与现有方法进行了比较，分析了与孕妇氧化应激相关的多种尿微量金属。

There are two major accomplishment this year. First, we developed a nonparametric and a semiparametric regression approach for bivariate failure time outcomes. This is motivated by the fact that many biomedical studies follow participants for multiple correlated health outcomes. Modeling these outcomes simultaneously can be more efficient than individual models, and allows us to characterize the risk of developing multiple diseases to facilitate risk prediction given individuals history of other diseases. In addition to two journal manuscripts describing these approaches, we also published a book by Chapman & Hall on this topic. Second, we developed an approach to handle assay limit of detection in environental 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.