COMMUNITY ENGAGEMENT RESEARCH CORE

这一核心是建立在假设中心的科学使命和实际效率将是 通过集中这些领域的专门知识和资源，大大加强了这一能力。虽然这项研究研究 在该中心内提出的建议在被研究人群、临床背景和 研究设计，它们由潜在的理论框架、重叠的测量策略、 以及致力于收集尽可能高质量的数据。为了充分利用 跨研究的共同特征，避免重复工作、集中数据管理、 计量和统计资源是必不可少的。 能够进行中心范围的分析是我们方法的另一个重要的增值功能。 将来自多个研究的相同数据组合在一起的汇集分析和比较分析都可以进行。例如，对核心数据中的仪器的心理测量学属性进行汇总分析 基数将使我们能够应用统计方法，而这些方法在有限的样本量下是不可能的 个别研究。同样，只有通过协调的理论驱动的测量和分析 我们将能够测试关于跨越不同健康终点的共同路径的假设， 包括抑郁症、肥胖症和传染病。这种方法将使我们能够探索重要的 关于哪些途径与特定健康结果唯一相关以及哪些途径与特定健康结果有关的问题 在健康结果中普遍存在。

This core is predicated on the assumption that the Center's scientific mission and practical efficiency would be significantly enhanced by centralizing expertise and resources in these areas. Although the research studies proposed within the Center are diverse with respect to the populations being studied, clinical contexts, and study designs, they are unified by an underlying theoretical framework, an overlapping measurement strategy, and a commitment to the collection of data of the highest quality possible. In order to fully take advantage of the common features across studies and avoid duplication of effort, centralized data management, measurement, and statistical resources are essential. The ability to carry out Center-wide analyses is another important value added feature of our approach. Both pooled analysis in which identical data from multiple studies are combined and comparative analysis can be carried out. For example, pooled analysis of the psychometric properties of instruments in the core data base will enable us to apply statistical methods that would not be possible by the limited sample size of individual studies. Similarly, only through a coordinated theoretically driven measurement and analytic approach will we be able to test hypotheses about common pathways across diverse health endpoints, including depression, obesity, and infectious disease. This approach will enable us to explore important questions regarding which pathways are uniquely linked to particular health outcomes and which pathways are common across health outcomes.