Analytical Methods: Environmental/Reproductive Epidemiology

说明(由调查员提供)：本申请是对先前资助的题为“分析方法：环境/生殖流行病学”的研究的续展申请。最初的供资周期促进了富有成效的合作，这些努力为研究揭示了新的方向，以更全面地涵盖在诸如密歇根多民族玻利维亚国研究(MIPBB)和西奈山女上班族研究(MSSWOW)等激励性研究下收集的暴露和生殖健康数据构成的多重挑战。正如在许多纵向研究中一样，MIPBB中使用的暴露分析随着时间的推移经历了演变，因此通过原始和最近的分析获得的数据以不同的分辨率水平进行记录。特别是，研究早期获得的数据主要是“堆积”的，这是因为分析限制有效地将值四舍五入到最接近的整数。对纵向数据的正确分析应根据记录数据所用的分析方法，将正确的分辨率级别赋予每个数据点。在流行病学研究中，观察到高度倾斜的暴露数据也是很常见的。严重偏斜、检测限度问题、随时间变化的分析分辨率以及因舍入而堆积的同时特征需要灵活和创新的建模，其最终目的是改进预测和有效确定暴露与生殖健康结果之间的关联(目标1)。到目前为止，我们的研究受到MSSWOW研究的推动，已经确定了研究怀孕时间和月经周期长度数据建模的新途径。在这样的研究中，怀孕时间通常是以周期数来记录的，而不是以天或周来测量的，因此需要离散数据生存分析的方法。需要模型创新，以便在这种情况下将环境暴露和其他协变量与生育率联系起来(目标2)。重复的月经周期长度数据往往不仅在平均长度上具有异质性，而且在变异性水平上也是如此。这促使需要灵活的建模和改进的方法来将女性分为月经周期长度和变异性亚组，并引起人们对潜在的错误分类错误的关注(Aim3)。这项续展申请继续寻求改进流行病学研究的分析方法，在环境和生殖健康领域的统计理论和应用之间取得有效的平衡。我们考虑了参数方法和半参数方法，指出这两种方法在这种情况下都有各自的优势，而且每种方法都有可能告知和加强另一种方法。虽然旨在对激励研究有直接好处，但将要开发的方法针对的是足够常见和基本的问题，使它们在统计和流行病学实践中具有更广泛的兴趣。公共卫生相关性环境暴露可对公共卫生的各个方面产生重大影响， 包括女性的生殖健康。这项应用旨在解决由两项具有里程碑意义的激励研究产生的暴露和生殖健康结果数据分析方面的多重独特挑战。即将开发的统计方法也将对公共卫生研究产生更广泛的影响，这些研究收集随着时间推移的暴露和结果数据。

DESCRIPTION (provided by investigator): This application constitutes a renewal application for the previously funded study entitled "Analytic Methods: Environmental/Reproductive Epidemiology". The initial funding cycle facilitated a productive collaboration, and these efforts have revealed promising new directions for research to more fully encompass the multiple challenges posed by exposure and reproductive health data collected under motivating studies such as the Michigan PBB Studies (MIPBB) and the Mount Sinai Study of Women Office Workers (MSSWOW). As in many longitudinal studies, exposure assays utilized in the MIPBB underwent an evolution over time so that data obtained via the original and more recent assays are recorded at different levels of resolution. In particular, data obtained earlier in the study were primarily "heaped", due to assay limitations that effectively led values to be rounded to the nearest integer. Proper analysis of the longitudinal data should attribute the correct level of resolution to each data point, based on the assay used to record it. In epidemiologic studies, it is also common to observe highly skewed exposure data. The simultaneous features of heavy skewness, detection limit issues, changing assay resolution over time, and heaping due to rounding require flexible and innovative modeling, with the ultimate aim of improved prediction and valid determination of associations between exposure and reproductive health outcomes (Aim 1). Our research to date motivated by the MSSWOW study has identified new avenues of research into the modeling of time-to-pregnancy and menstrual cycle length data. In such studies, time-to-pregnancy is typically recorded in terms of a number of cycles as opposed to being measured in days or weeks, so that methods for discrete data survival analysis are required. Modeling innovations are needed in order to relate environmental exposures and other covariates to fertility in such contexts (Aim 2). Repeated menstrual cycle length data tend to be characterized by heterogeneity not only in average length, but in the level of variability as well. This motivates a need for flexible modeling and improved methods for classifying women into menstrual cycle length and variability subgroups, and brings attention to potential misclassification error (Aim3). This renewal application continues to seek improved analytic methods for epidemiologic research by means of an effective balance between statistical theory and application in the environmental and reproductive health areas. We consider both parametric and semi-parametric approaches, noting that both have their advantages in this context and that each approach has the potential to inform and augment the other. While intended to be of direct benefit to the motivating studies, the methods to be developed address issues that are common and fundamental enough to make them of broader interest in statistical and epidemiologic practice. PUBLIC HEALTH RELEVANCE Environmental exposures can have a major impact on various aspects of public health, including women's reproductive health. This application aims to address multiple unique challenges in the analysis of exposure and reproductive health outcome data stemming from two landmark motivating studies. The statistical methods to be developed will also have broader implications toward public health studies that collect exposure and outcome data over time.