Statistical Services and Analysis Resource

统计服务和分析资源摘要 环境健康研究表明，在关键时期， 在整个人的生命周期中，发育阶段可以表现为疾病或功能障碍。事实上，人类 暴露于从内部生理学（例如，氧化应激）对外 化学的（例如，空气污染、消费品）和生活方式因素，包括暴露于环境 污染物、营养不良和压力。这些因素是多维度的、复杂的--不是简单的、单一的 与单一健康结果相关的暴露。用于分析复合物的统计方法和研究设计， 在这种环境中出现高维数据仍然相对较新， 统计技术。统计服务和分析资源（SSAR）整合了多学科 专门知识（统计学，流行病学，生物信息学）在一个单一的学院提供的合作与 更广泛的环境健康研究社区通过数据中心。特别行政区的目标有三个 主要类别：咨询和分析支持，HHEAR任务相关方法开发， 实施新方法，支持环境卫生领域的合作研究 研究财团。我们的具体目标是：1）提供统计，流行病学和生物信息学 利用HHEAR实验室网络资源为研究者提供咨询和分析支持; 2） 促进在相关领域应用新的统计、分析和生物信息学方法， HHEAR和数据中心; 3）通过统计，促进目标2中这些方法的实施 培训环境卫生研究人员，创建可公开获取的统计软件， HHEAR数据和开发公开可用的培训/示例数据集，用于教学式学习 通过与儿童环境健康有关的网络研讨会;以及4）支持跨 HHEAR联盟通过进行与环境暴露相关的初步荟萃分析， 相关专家联盟内的疾病状况;以及支持其他合作研究 可能通过HHEAR研究者联盟发起的项目。实现这些 目标将推进HHEAR数据中心和更大的HHEAR网络的目标， 了解环境暴露对人类健康的潜在影响， 创新的生物统计/生物信息学方法，用于分析环境健康数据。

STATISTICAL SERVICES AND ANALYSIS RESOURCE SUMMARY Research in environmental health demonstrates that consequence of environmental exposures during critical periods in development can manifest as disease or dysfunction across the human life span. Indeed, humans are exposed to environmental factors ranging from internal physiology (e.g., oxidative stress) to external chemical (e.g., air pollution, consumer products) and lifestyle factors including exposures to environmental contaminants, poor nutrition, and stress. These factors are multi-dimensional and complex – not simple, single exposures related to a single health outcome. Statistical methods and study designs for analyzing the complex, high-dimensional data that arise in such settings are still relatively new and require knowledge of advanced statistical techniques. The Statistical Services and Analysis Resource (SSAR) consolidates multi-disciplinary expertise (statistics, epidemiology, bioinformatics) in a single faculty made available for collaboration with the broader environmental health research community through the Data Center. The aims of the SSAR are in three primary categories: consultation and analysis support, HHEAR mission-related methods development and implementation of novel methods, and support of collaborative research across the Environmental Health Research consortium. Our specific aims are: 1) provide statistical, epidemiological, and bioinformatics consultation and analysis support to investigators utilizing the HHEAR Laboratory Network resources; 2) catalyze the application of novel statistical, analytical, and bioinformatics methodologies in relevant areas to the HHEAR and the Data Center; 3) catalyze the implementation of these methods in Aim 2 through statistical training for environmental health researchers by creating publicly available statistical software for analysis of the HHEAR data and developing publicly available training/example datasets to be used for didactic learning through webinars related to children's environmental health; and 4) support collaborative research across the HHEAR consortium by conducting initial meta-analyses relating environmental exposures and selected disease conditions within a consortium of relevant experts; and supporting other collaborative research projects that may initiate through the HHEAR consortium of research investigators. The achievement of these aims will advance the goals of the HHEAR Data Center and the larger HHEAR Network to maximize our understanding of the potential impact of environmental exposures on human health by providing rigorous and innovative biostatistical/bioinformatics methods for analyzing environmental health data.