Nonparametric Bayes Methods for Biomedical Studies

生物医学研究的非参数贝叶斯方法

基本信息

批准号：
8248216
负责人：
David Brian Dunson
金额：
$ 24.08万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-05-15 至 2014-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8248216
关键词：
Accounting Address Adult Age Animals Birth Weight Chronic Disease Chronic stress Collaborations Collection Complex DNA Methylation Data Data Analyses Data Set Dependence Dependency Development Diabetes Mellitus Diet Environment Environmental Exposure Epidemiologic Studies Equation Exposure to Genetic Predisposition to Disease Goals Growth Health Hormones Human Hydrocortisone Individual Infant Infant Health Infectious Pregnancy Complications Infertility Intervention Lead Letters Life Literature Longitudinal Studies Low Birth Weight Infant Measures Menstrual cycle Methodology Methods Modeling Mothers Motivation Multivariate Analysis Nutrient Obesity Onset of illness Outcome Ovulation Prediction Oxidative Stress Pathway interactions Pattern Physiological Postpartum Period Pregnancy Pregnancy Outcome Premenopause Process Progesterone Property Prospective Studies Psychosocial Stress Questionnaires Relative (related person)Reproduction Reproductive Health Research Personnel Risk Role Smoking Smoking Status Societies Statistical Methods Stress Structure Testing Time Vitamins Weight Weight Gain Woman Women&apos s Health Work base disorder risk high risk improved independent component analysis innovation insight interest lifestyle factors novel strategies nutrition obesity risk predictive modeling prospective reproductive function reproductive hormone response tool trait

项目摘要

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.

描述（由申请人提供）：我们建议开发新的统计方法，以改进对生物医学研究的多变量、纵向和功能数据的分析。人们越来越担心，在关键窗口期间发生的暴露可能会导致以后的不良健康影响，这促使前瞻性研究收集有关多次随时间变化的暴露和健康结果的详细数据。需要新的统计方法来有效地发现此类高维数据集中的关键窗口和时变依赖性，同时限制错误发现。这些方法可能会对暴露引起不良健康影响的机制产生根本性的新见解，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，以灵活地表征个体之间功能数据的差异，例如氧化应激、生殖激素、营养物质和妊娠体重随时间的变化轨迹。 2. 开发基于多个时变因素灵活预测健康反应的方法，同时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月经周期中氧化应激、营养物质和生殖激素之间的关系，以解释年龄、肥胖和吸烟的作用。还可以考虑应用以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：不孕症和糖尿病等不良健康状况的发生取决于遗传倾向和各种生活方式因素（包括饮食和环境暴露）之间的相互作用。这些因素随年龄的变化是风险的重要决定因素，因为关键窗口可能在疾病发作前许多年出现。我们提供必要的统计工具来确定关键的暴露窗口，以便通过有针对性的干预措施来降低风险。