Analytical Methods: Environmental/Reproductive Epidemiology

描述（由研究者提供）：本申请是先前资助的研究“分析方法：环境/生殖流行病学”的续期申请。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，可以更充分地涵盖暴露所带来的多重挑战，以及在诸如密歇根多氯环溴环研究（MIPBB）和西奈山女性办公室工作人员研究（MSSWOW）等激励研究中收集的生殖健康数据。与许多纵向研究一样，MIPBB中使用的暴露测定随着时间的推移经历了演变，因此通过原始和最近的测定获得的数据以不同的分辨率记录。特别是，由于检测限制，研究早期获得的数据主要是“堆积”的，这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用于记录数据的分析方法，为每个数据点赋予正确的分辨率水平。在流行病学研究中，观察到高度扭曲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舍入导致的堆积等同时存在的特征需要灵活和创新的建模，其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确定（目标1）。迄今为止，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。在这类研究中，怀孕时间通常以若干周期来记录，而不是以天或周来测量，因此需要离散数据生存分析的方法。在这种情况下，为了将环境暴露和其他协变量与生育率联系起来，需要进行建模创新（目标2）。重复的月经周期长度数据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，以将女性划分为月经周期长度和变异性亚组，并引起了对潜在误分类误差的关注（Aim3）。这项新的应用继续通过统计理论与环境和生殖健康领域的应用之间的有效平衡，寻求改进流行病学研究的分析方法。我们考虑了参数方法和半参数方法，注意到两者在这种情况下都有其优势，并且每种方法都有可能通知和增强另一种方法。虽然这些方法的目的是对激励研究有直接的好处，但要开发的方法解决的问题是常见的和基本的，足以使它们在统计和流行病学实践中引起更广泛的兴趣。与公共卫生有关的环境暴露可对公共卫生的各个方面产生重大影响。

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.