CORE--Clinical, Biostatistical and Data Management

目标是为招募合格受试者、收集临床数据提供综合资源 和生物样本，并提供设计和数据分析服务，以支持所有PPG研究者。此外，我们还将提供全面的数据管理服务。我们将 通过协助研究设计、生物统计分析、iof结果解释和手稿准备，提供广泛的合作。特别是，共同研究者将提供功能基因组学的专业知识，用于表征高维分子水平数据和临床结果之间关系的分析方法，来自微阵列技术的基因表达数据的分析方法，以及用于分析蛋白质组学数据的创新方法。具体目标是：1。临床：（a）继续先前的急性肺损伤中的NHLBI SCOR（AL1 SCOR）严重创伤患者的前瞻性队列（"创伤队列"），所述严重创伤患者在创伤的5天内处于发展ALL的高风险;（B） 利用创伤队列提供合格的受试者和生物样品;（c）继续先前的ALISCOR系统以从患者收集临床数据和生物样品 参加了创伤队列。2.生物统计学：（a）就研究设计问题，包括样本量和把握度的考虑，向研究人员提供咨询意见;（B）对实验室项目的数据进行统计分析，并为临床研究开发风险因素模型，以解决特定项目提案中定义的特定研究假设;（c）协助编写中期和最终报告、演示文稿、摘要， （d）进行可能导致产生新假设的探索性分析。 3.数据管理：（a）修改和实施创伤组群数据管理系统;（B）为研究人员提供培训和援助;（c）开展 临床研究的质量保证、验证、查询解决和报告;（d）创建高科学质量的有效数据集，将多个来源的数据结合起来进行生物统计分析。

The goal is to provide integrated resources for enrolling eligible subjects, collecting clinical data and biological samples, and providing design and data analytic services to support all PPG investigators. Moreover, we will provide comprehensive data management services. We will provide a broad range of collaborations by assisting with study design, biostatistical analyses, interpretation iof results, and manuscript preparation. In particular, co-investigators will provide specialized expertise in functional genomics, analytic methods for characterizing the relationships among high-dimensional molecular level data and clinical outcomes, analytic methods for gene expression data arising from microarray technologies, and innovative methods for analysis of the proteomics data. The Specific Aims are: 1. Clinical: (a) Continue the previous NHLBI SCOR in Acute Lung Injury (ALl SCOR) prospective cohort of major trauma patients ("Trauma Cohort") who are at high risk for developing ALl within 5 days of trauma; (b) utilize the Trauma Cohort to provide eligible subjects and biological samples; (c) continue the previous ALl SCOR's system to collect clinical data and biological samples from patients enrolled in Trauma Cohort. 2. Biostatistical: (a) Advise investigators on study design issues, including sample size and power considerations; (b) conduct statistical analyses of data from laboratory projects and risk factor model development for clinical investigations to address specific research hypotheses defined in the project-specific proposals; (c) assist with preparation for and writing of interim and final reports, presentations, abstracts, and manuscripts; (d) conduct exploratory analyses that may lead to generation of new hypotheses. 3. Data Management: (a) Modify and implement the Data Management System (DMS) for the Trauma Cohort; (b) provide training and assistance for research staff; (c) conduct all phases of quality assurance, validation, query resolution and reporting for clinical studies; (d) create valid datasets of high scientific quality, combining data from multiple sources for biostatistical analyses.