Nonparametric Bayes Methods for Biomedical Studies

描述（由申请人提供）：我们建议开发新的统计方法来改进生物医学研究的多变量、纵向和功能数据的分析。越来越多的人担心，在关键窗口期发生的接触可能导致后来的不良健康影响，这促使人们开展前瞻性研究，收集关于多种时变接触和健康结果的详细数据。需要新的统计方法来有效地发现这些高维数据集中的关键窗口和时变依赖关系，同时限制错误发现。这些方法可能使我们对暴露导致不利健康影响的机制有了根本性的新认识，同时也允许制定有针对性的干预措施和更准确地预测疾病风险。我们的目标包括以下内容。1. 开发非参数贝叶斯统计方法，灵活表征个体在功能数据上的差异，如氧化应激、生殖激素、营养和妊娠体重随时间的变化轨迹。2. 开发基于多个时变因素的灵活预测健康反应的方法，同时也估计关键窗口并发现不同因素之间的动态关系。3. 应用这些方法来评估氧化应激、营养和生殖激素在月经周期中的关系，以及年龄、肥胖和吸烟的作用。还考虑应用于确定与短期婴儿健康结果相关的妊娠体重增加模式。公共卫生相关性：诸如不孕症和糖尿病等不良健康状况的发展取决于遗传易感性与各种生活方式因素（包括饮食和环境暴露）之间的相互作用。随着年龄的增长，这些因素的变化是风险的重要决定因素，因为关键窗口期可能发生在疾病发病前很多年。我们提供必要的统计工具来确定暴露的关键窗口，以便通过有针对性的干预措施降低风险。

DESCRIPTION (provided by applicant): We propose to develop new statistical methods for improving analyses of multivariate, longitudinal and functional data from biomedical studies. There is increasing concern that exposures occurring during critical windows can lead to later adverse health effects, motivating prospective studies collecting detailed data on multiple time-varying exposures and health outcomes. New statistical methods are needed to efficiently discover critical windows and time-varying dependencies in such high-dimensional data sets, while limiting false discoveries. These methods may lead to fundamental new insights into mechanisms by which exposures induce adverse health effects, while also allowing for the development of targeted interventions and more accurate predictions of disease risk. Our goals include the following. 1. Develop nonparametric Bayes statistical methods for flexibly characterizing differences among individuals in functional data, such as trajectories over time in oxidative stress, reproductive hormones, nutrients and pregnancy weight. 2. Develop methods for flexibly predicting a health response based on multiple time- varying factors, while also estimating critical windows and discovering dynamic relationships between the different factors. 3. Apply these methods to assess relationships between oxidative stress, nutrients and reproductive hormones over the menstrual cycle accounting for the role of age, obesity and smoking. Also consider applications to identify patterns of pregnancy weight gain associated with short-term infant health outcomes. PUBLIC HEALTH RELEVANCE: The development of adverse health conditions, such as infertility and diabetes, depends on the interaction between genetic predisposition and a variety of lifestyle factors, including diet and environmental exposures. Changes with age in these factors is an important determinate of risk, as critical windows can occur many years before disease onset. We provide the statistical tools necessary to identify critical windows of exposure in order to reduce risk through targeted interventions.