Analytical Methods: Environmental/Reproductive Epidemiology

描述（由研究者提供）：本申请构成先前资助的题为“分析方法：环境/生殖流行病学”的研究的更新申请。最初的供资周期促进了富有成效的合作，这些努力揭示了有希望的新研究方向，以更充分地涵盖在密歇根州多溴联苯研究和西奈山女性办公室工作人员研究等激励性研究中收集的接触和生殖健康数据所带来的多重挑战。正如在许多纵向研究中一样，MIPBB中使用的暴露测定随着时间的推移而发生变化，因此通过原始和更新的测定获得的数据以不同的分辨率水平记录。特别是，研究早期获得的数据主要是“堆积”的，这是由于试验限制，有效地导致数值四舍五入到最接近的整数。纵向数据的正确分析应根据记录数据的检测方法，将正确的分辨率水平归因于每个数据点。在流行病学研究中，观察到高度偏斜的暴露数据也很常见。严重偏度、检测限问题、随时间变化的测定分辨率以及由于舍入而导致的堆积等同时存在的特征需要灵活和创新的建模，最终目的是改进暴露与生殖健康结果之间关联的预测和有效测定（目标1）。迄今为止，我们在MSSWOW研究的推动下进行的研究已经确定了怀孕时间和月经周期长度数据建模的新研究途径。在这些研究中，妊娠时间通常以周期数记录，而不是以天或周测量，因此需要离散数据生存分析的方法。在这种情况下，需要进行建模创新，以便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具有异质性。这激发了对灵活建模和改进方法的需求，用于将女性分类为月经周期长度和变异性亚组，并引起对潜在误分类错误的关注（Aim 3）。这一更新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实现有效平衡。我们认为参数和半参数的方法，注意到这两种方法都有自己的优势，在这种情况下，每种方法都有可能通知和增强其他。虽然旨在直接受益于激励性研究，但要开发的方法解决了常见和基本的问题，足以使它们在统计和流行病学实践中引起更广泛的兴趣。环境暴露可对公共卫生的各个方面产生重大影响， 包括妇女的生殖健康。该应用程序旨在解决来自两项具有里程碑意义的激励研究的暴露和生殖健康结果数据分析中的多个独特挑战。待开发的统计方法也将对收集暴露和结果数据的公共卫生研究产生更广泛的影响。

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.