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 通过建立和维护 用于存储、共享和管理数据的安全集中式系统；设计稳健的研究；并分析临床， 三个 PPG 项目的机械和高通量数据。 DMAC 将与核心和项目合作 确保临床和机制研究数据的分析完整且严格， 使用兼容的定义和指标进行顺利集成，以提供可解释的发现并推进 该计划的临床和机制研究。我们的目标如下。目标 1. 提供最先进的 安全、集成和交互式数据库，包含该计划中的项目生成的高质量数据 使研究人员能够广泛接触。 DMAC 将建立一个研究电子数据采集系统 (REDCap) 用于存放研究数据，扩展帕克中心最先进的生物样本管理 软件平台，提供集成项目 1-3 和 HEMC 数据的 PPG 平台。 REDCap 将 将为此计划项目收集的生物样本数据与安全的相关患者元数据相集成 用于人体组织研究。 DMAC将对所有PPG数据进行质量控制并进行分析，以确保数据 元素是干净的。目标 2. 跨该计划中的项目设计科学合理且稳健的研究。的 DMAC 由具有设计临床试验、纵向研究和研究方面专业知识的人员组成 包括机械终点。核心将利用基于团队科学的方法来利用这些专业知识， 提炼可行的问题和假设，以制定科学合理的分析计划 现代统计工具有助于得出可解释且有意义的发现，并证明数字的合理性 通过考虑期间可用的资源来解决科学问题所需的样本和参与者 指定的学习时间表。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 调查人员建立长期合作关系 进入研究人员的研究团队，领导研究的数据科学部分。使用完善的团队 基于科学的方法，核心成员将制定和实施统计分析计划来解决项目问题 旨在确保研究结果可重复。 DMAC 将对 PPG 研究人员进行生物统计学和 与他们的研究最相关的生物信息学，以促进生产中的有效合作 跨学科研究出版物。 DMAC 与 HEMC 一起提供一些最具创新性的 对大数据集进行生物信息和统计分析的工具，以做出最广泛和最具变革性的研究 对理解空气污染相关临床表型与机制研究的影响 病理学。

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.