Statistical Methods In Epidemiology--general

该项目为流行病学开发新的统计方法，具有广泛的应用，并为正在进行的流行病学项目，特别是与生殖研究有关的项目开发所需的方法。 今年的工作涉及两个主要项目。(1)一个项目涉及基于人类样本的昂贵生物标志物的汇总评估。早期的工作表明，在病例对照设置中，可以将病例组和对照组的样本汇集在一起，并进行基于集合的分析。 通过略微修改的逻辑模型，该分析可以估计个体水平的风险参数，并且与基于个体测定的分析相比几乎没有损失功效。 这意味着，如果暴露是基于使用人体样本的昂贵测定，则可以通过在测定前合并样本来显著提高效率。在最近的工作中，我们将这些方法扩展到在至事件发生时间数据（例如至妊娠时间研究）的背景下使用结局依赖性汇总。虽然这些方法可以节省大量成本并节省宝贵的样本，但这些方法施加的限制限制了模型的灵活性。 例如，无法探索次要终点，也无法拟合非线性剂量-对数-反应模型。我们现在正在做更多的工作，旨在开发“前瞻性汇集”的半参数方法，这将克服由于需要使用结果依赖性汇集而造成的一些限制。我们现在正在开发半参数方法，用于事件发生时间设置中的“前瞻性汇总”。 这种方法应能对环境暴露对生存和其他故障时间结果的影响进行有力和廉价的评估。 (2)第二个项目是开发与环境接触有关的结果分析方法，其中接触生物标志物通常低于检测限。 可以采用为生存分析开发的方法，并允许混杂因素调整。 我们将其应用于国家健康和营养检查调查数据，这些数据将血液中的有毒分析物与自身免疫的生物标志物抗核抗体的存在相关。 我们在美国流行病学杂志上发表了一篇正在修订的描述这种方法的论文。 在未来的工作中，我们希望将这项工作扩展到化学品的混合物，利用现有的毒性当量因子来创建一个审查的毒性加权平均值。

This project develops new statistical methods for epidemiology with broad applications and also methods as needed for ongoing projects in epidemiology, particularly those related to reproductive studies. The work this year involved two main projects. (1) One project concerns pooled assessment of expensive-to-assay biomarkers based on human samples. Earlier work had shown that in a case-control setting one can pool together specimens from sets of cases and sets of controls and carry out a set-based analysis. With a slightly modified logistic model that analysis can estimate the individual-level risk parameters and loses almost no power compared to analysis based on individual assays. This means that if an exposure is based on an expensive assay that uses human samples, one can markedly improve efficiency by pooling specimens prior to assay. In recent work, we have extended these methods to apply using outcome-dependent pooling in the context of time-to-event data, e.g. time to pregnancy studies. Although these methods can produce huge cost savings and spare precious samples, the methods impose restrictions that limit the flexibility of models that can be fit. For example, one cannot explore secondary endpoints and one cannot fit nonlinear dose-logit-response models. We are now doing additional work aimed at developing semi-parametric methods for "prospective pooling," which will overcome some of the limitations caused by the need to use outcome-dependent pooling. We are now developing semi-parametric methods for "prospective pooling" in time-to-event settings. Such methods should allow powerful and inexpensive assessments of the effects of environmental exposures on survival and other failure-time outcomes. (2) A second project is developing methods for analysis of outcomes related to environmental exposures where the exposure biomarker is often at a level below the assay limit of detection. Methods developed for survival analysis can be employed and permit confounder adjustment. We are applying this to data from the National Health and Nutrition Examination Survey data relating toxic analytes in blood to the presence of a biomarker, anti-nuclear antibodies, for autoimmunity. We have a paper describing this method under revision for the American Journal of Epidemiology. In future work we hope to extend this work to mixtures of chemicals, making use of existing toxic equivalency factors to create a censored toxicity-weighted average.