Analytic Methods:Enviornmental/Reproductive Epidemiology

描述(由申请人提供)：旨在进行更精确和准确的暴露评估，并确保暴露-反应关联估计的有效性的分析方法，对流行病学研究至关重要。鉴于需要解决的科学问题以及所获得的数据往往复杂的结构和性质，这一事实与环境和生殖研究特别相关。这项申请从密歇根州女性健康研究(MFHS)和西奈山女性上班族研究(MSSWOW)这两项研究中得出了具体的动机。在这些研究中产生的假设需要研究改进的和有针对性的方法来预测混合线性模型中的随机效应，研究在调整测量误差的同时将环境暴露与生殖结果相关联的方法，以及研究纵向月经周期长度数据的建模。 利用20世纪70年代中期接触多溴联苯(PBB)的女性MFHS参与者的数据，我们提出了一个线性混合模型来描述血清PBB水平随时间的下降，同时考虑了重要的固定和时间相关协变量(例如，干预怀孕)。该模型为研究预测每个符合条件的妇女在指数妊娠时的残留多溴联苯水平的最佳标准奠定了基础。这些方法将扩展到考虑低于化验检测限值的多溴联苯测量。其次，我们建议同时对MFHS参与者和/或他们的女儿的PBB测量和生殖健康结果进行建模，以获得对暴露-反应关联的有效和一致的估计，同时考虑测量误差。最后，我们使用MSSWOW的数据提出了混合模型来描述标准和非标准月经周期长度的分布，并描述了新的方法来解释女性内部的随机异质性和协变量在这一背景下的影响。 这一应用程序旨在通过结合统计学理论的最新发展，开发新的方法，并将这一努力集中于重要的科学研究，来改进流行病学研究的分析方法。这些发展将直接惠及环境和生殖研究，但它们的普遍程度足以对统计实践作出普遍有益的贡献。

DESCRIPTION (provided by applicant): Analytic methods geared toward more precise and accurate exposure assessments, and toward ensuring validity in the estimation of exposure-response associations, are critical to epidemiologic studies. Given the scientific questions to be addressed and the often complex structure and nature of the data obtained, this fact is particularly pertinent to environmental and reproductive studies. This application draws specific motivation from two such studies, the Michigan Female Health Study (MFHS) and the Mount Sinai Study of Women Office Workers (MSSWOW). Hypotheses arising in these studies require research into improved and targeted methods for predicting random effects in mixed linear models, into methods for associating environmental exposures to reproductive outcomes while adjusting for measurement error, and into the modeling of longitudinal menstrual cycle length data. Using data collected on female MFHS participants who were exposed to polybrominated biphenyls (PBB) in the mid-1970s, we propose a linear mixed model to describe the decay of serum PBB levels over time while accounting for important fixed and time-dependent covariates (e.g., intervening pregnancies). This model forms the basis for research into optimal criteria for predicting the residual PBB level for each eligible woman at the time of an index pregnancy. These methods will be extended to account for PBB measurements that are below assay detection limits. Secondly, we propose simultaneous modeling of PBB measurements and reproductive health outcomes among MFHS participants and/or their daughters to obtain valid and consistent estimation of exposure-response associations while accounting for measurement error. Finally, we propose mixture models to describe the distribution of standard and non-standard menstrual cycle lengths using data from the MSSWOW, and we describe novel approaches to account for random within-woman heterogeneity and the effects of covariates in this context. This application is designed to improve analytic methods for epidemiologic research by incorporating current developments in statistical theory, by developing new methodology, and by targeting this effort toward important scientific studies. These developments will directly benefit environmental and reproductive studies, but they are ubiquitous enough to be generally useful contributions to statistical practice.