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

点击翻译按钮获取中文摘要

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.