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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：
8451617
负责人：
David Brian Dunson
金额：
$ 23.6万
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 2015-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8451617
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.

描述(由申请人提供)：我们建议开发新的统计方法，以改进来自生物医学研究的多变量、纵向和功能性数据的分析。人们越来越担心，在关键窗口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关于多个时变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这种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。这些方法可能导致对暴露导致不利健康影响的机制的根本新见解，同时也允许开发有针对性的干预措施和更准确的疾病风险预测。我们的目标包括以下几个方面。1.开发非参数贝叶斯统计方法，灵活地描述个体之间在功能数据上的差异，例如氧化应激、生殖激素、营养素和怀孕体重随时间的轨迹。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、营养物质和生殖激素在月经周期中的关系，并考虑到年龄、肥胖和吸烟的作用。还可以考虑应用程序来确定与短期婴儿健康结果相关的妊娠体重增加模式。