Data Management and Analysis Core (DMAC) for the Air pollution disrupts Inflammasome Regulation in HEart And Lung Total Health (AIRHEALTH) Study

摘要：数据管理和分析核心（DMAC） 数据管理和分析核心（DMAC）以及为科学研究提供支持的总体目标 人类生物标志物暴露监测核心（HEMC）和项目1 - 3，建立和维护一个 安全的集中式系统来存放、共享和管理数据;设计稳健的研究;分析临床， 三个PPG项目的机械和高通量数据。DMAC将与核心和项目 为了确保完整和严格地进行临床和机理研究数据的分析， 使用兼容的定义和指标进行平滑整合，以提供可解释的结果， 临床和机制研究。我们的目标如下。目标1。提供最先进的 安全，集成和交互式的高质量数据库，由项目中的项目生成， 能够广泛接触研究者。DMAC将建立一个研究电子数据采集系统 （REDCap）存储研究数据，扩展了帕克中心最先进的生物标本管理 软件平台，以提供集成项目1 - 3和HEMC数据的PPG平台。REDCap将 将为该计划项目收集的生物标本数据与安全的相关患者元数据相结合 用于人体组织的研究DMAC将对所有PPG数据和分析进行质量控制，以确保 元素是干净的。目标二。在项目中设计科学合理和稳健的研究。的 DMAC由在设计临床试验、纵向研究和研究方面具有专业知识的人员组成， 包括机械终点。核心小组将采用以科学为基础的方法，利用这些专门知识， 完善问题和假设，以制定科学合理的分析计划， 现代统计工具，使自己的解释和有意义的结果，并证明数字 通过考虑到可利用的资源， 指定的研究时间轴。目标3。对临床、机制和高通量数据进行分析。的 DMAC将通过嵌入核心成员与PPG研究者建立长期合作关系 进入研究人员的研究团队，领导研究的数据科学部分。使用一个完善的团队 核心成员将制定和实施统计分析计划，以解决项目 目的是确保结果是可重复的。DMAC将对PPG研究者进行生物统计学教育， 生物信息学最相关的研究，以促进有效的合作，在生产 跨学科研究出版物。与HEMC一起，DMAC提供了一些最具创新性的 大型数据集的生物信息学和统计分析工具， 对理解空气污染相关的临床表型与机制研究的影响 病理

ABSTRACT: DATA MANAGEMENT AND ANALYTICS CORE (DMAC) Data Management and Analytics Core (DMAC) and the overall goals that provide support to the Scientific Human Biomarker Exposure Monitoring Core (HEMC) and Projects 1-3 by establishing and maintaining a secure centralized system to house, share, and manage data; designing robust studies; and analyzing clinical, mechanistic, and high-throughput data for the three PPG projects. DMAC will work with the cores and projects to ensure that analyses resulting from clinical and mechanistic study data are performed with integrity and rigor, using compatible definitions and metrics for smooth integration to provide interpretable findings and advance clinical and mechanistic research for the program. Our aims are as follows. Aim 1. Provide a state-of-the-art secure, integrated and interactive database of high-quality data generated by projects in the program that enables wide access to study investigators. The DMAC will build a system in Research Electronic Data Capture (REDCap) to house study data that extends the Parker Center's state-of-the-art biospecimen management software platform to provide a PPG platform that integrates data from Projects 1-3 and the HEMC. REDCap will integrate data from biospecimens collected for this program project with secured, associated patient metadata for research on human tissues. DMAC will conduct quality control for all PPG data and analyses to ensure data elements are clean. Aim 2. Design scientifically sound and robust studies across projects in the program. The DMAC consists of personnel with expertise in designing clinical trials, longitudinal studies, and studies that include mechanistic endpoints. The core will leverage such expertise using a team science-based approach and refining questions and hypotheses that are feasible to develop scientifically sound analysis plans using the most modern statistical tools that lend themselves to interpretable and meaningful findings, and to justify the number of samples and participants necessary to address scientific questions by considering resources available during the specified study timeline. Aim 3. Conduct analyses of clinical, mechanistic, and high throughput data. The DMAC will develop long-term collaborative relationships with PPG investigators by embedding core members into investigators' research teams to lead the data science component of studies. Using a well-established team science-based approach, core members will develop and implement statistical analysis plans to address project aims to assure findings are reproducible. The DMAC will educate PPG investigators in biostatistics and bioinformatics most pertinent to their research to promote effective collaboration in the production of interdisciplinary research publications. Together with the HEMC, the DMAC offers some of the most innovative tools for bioinformatic and statistical analysis of large data sets to make the broadest and most transformative impact in understanding clinical phenotype associations with mechanistic studies in air-pollution-associated pathology.