Analytic Methods:Enviornmental/Reproductive Epidemiology

描述（由申请人提供）：分析方法旨在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，对流行病学研究至关重要。鉴于所要处理的科学问题和所获得的数据的结构和性质往往很复杂，这一事实与环境和生殖研究特别有关。这一应用从两个这样的研究中获得了具体的动机，密歇根女性健康研究（MFHS）和西奈山女性办公室工作人员研究（MSSWOW）。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联系起来的方法，以及研究纵向月经周期长度数据的建模。

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.