Modeling Complex Exposures and Reproductive Outcomes

描述（由申请人提供）：调查人员经常竭尽全力获得仔细、详细的暴露测量，这些测量可能有多个维度，并可能随时间变化，例如饮食、压力或血压。在评估个人暴露史与健康事件发生时间或发生率之间的关系时，重要的是降低这种多变量暴露史数据的维度，以提高统计能力。虽然用简单的摘要代替多变量暴露信息（在实践中通常是这样做的）有时可以提高可解释性和统计能力，但通常不清楚如何最好地总结手头的信息。此外，将详细数据简化为幼稚的总结往往与获得尽可能准确的暴露评估的研究目标背道而驰。我们感兴趣的是开发和应用统计方法，允许以客观的方式构建基于证据的总结，以最大化有关感兴趣的结果的信息。根据妊娠、感染和营养（PIN）研究的数据，一项关于早产的前瞻性队列研究，我们提出了一个多期暴露过程和生殖结果的贝叶斯分层模型。关于暴露时间、持续时间和强度的数据使用一个框架内的共享脆弱性术语进行总结，该框架考虑了随时间变化的过程、相关暴露度量和缺失数据。对暴露效应的推断可以基于使用高效MCMC算法获得的后验密度。这些方法将利用妊娠、感染和营养研究数据应用于阴道出血和妊娠持续时间，但在生殖健康、流行病学和其他领域也有广泛的应用。

DESCRIPTION (provided by applicant): Investigators often go to great lengths to obtain careful, detailed measures of exposure, which may have multiple dimensions and may change over time, e.g. diet, stress, or blood pressure. In assessing the association between an individual's exposure history and the time or rate of occurrence of a health event, it is important to reduce the dimensionality of this multivariate exposure history data in order to increase statistical power. Although replacing the multivariate exposure information with a simple summary, as is typically done in practice, can sometimes improve interpretability and statistical power, it is typically not clear how best to summarize the information at hand. In addition, reducing detailed data into naive summaries often runs counter to the study goals of obtaining the most accurate assessment of exposure possible. We are interested in developing and applying statistical methods, allowing evidence-based summaries to be constructed objectively in a manner that maximizes information about the outcomes of interest. Motivated by data from the Pregnancy, Infection, and Nutrition (PIN) study, a prospective cohort study of preterm birth, we propose a Bayesian hierarchical model for a multiple episode exposure process and a reproductive outcome. Data on timing, duration, and intensity of exposure are summarized using a shared frailty term within a framework that accounts for changes in the process over time, correlated exposure measures, and missing data. Inferences on exposure effects can be based on the posterior density obtained using an efficient MCMC algorithm. The methods will be applied to vaginal bleeding and duration of gestation using the Pregnancy, Infection, and Nutrition study data but have broad applications in reproductive health, epidemiology, and other areas.