DATA AND STATISTICS CORE

B。数据和统计核心 A.主要目标和责任。数据和统计核心有三个目标。 首先，我们将提供最先进的统计咨询和支持，以测试科学假设的 项目第二，我们将开发新的统计方法，以应对方法上的挑战 科学项目的新颖方面。这后一项努力将涉及我们密切的互惠关系的继续。 与该中心的科学方案的领导人的工作关系，如下所述。三是 承担人体临床研究的数据管理责任，并将开发数据库， 整合啮齿动物和人类研究的数据集，如科学所示。通过数据管理 这一核心将在保持评估与评估之间的整合和协作方面发挥关键作用， 核心项目和个别项目，并为执行/行政委员会跟踪科学进展情况， ACE中心数据和统计核心在拟议的 研究，并将继续在执行和数据分析阶段的建议， research.由于项目本质上是翻译性的，因此核心项目和项目的相关组成部分可以 在项目和整个中心的层面上充分发挥协同作用。 我们将通过履行以下职责来实现这些广泛目标： 1.我们将与项目合作，实施最先进的数据分析策略。 2.我们将开发遗传研究的数据分析方法， 基因型-表型连锁和关联研究，fMRI研究，纵向研究， 治疗效果和药物遗传学建模。 3.我们将与项目合作，建立新的数据分析方法的可靠性和有效性，并 发表对自闭症研究领域有价值的新方法。 4.我们将与评估核心合作，不断努力开发更高效、可靠和 用于诊断和治疗结果评估的心理测量学上合理的临床测量。与 我们中心的重点是坚持相同性（IS），我们预计，努力发展 这方面的措施将是本核心的一项持续承诺。 5.我们将从评估核心（包括数据库）提供临床数据的集中管理 开发、验证、质量控制程序，以及数据访问、安全和 临床数据的归档。我们亦会协助进行项目2和项目4的鼠患研究， 开发数据库，并将小鼠和大鼠的定量观察结果与临床结果相结合， 在我们的转化研究的背景下，将成为可能的方式发现。 这些目标将通过以下方式实现： 1.利用ACCESS软件维护和开发数据库结构; 2.应用ACE范围内的标准化数据传输和数据管理方法; 3.定期将所有数据库存档; 4.执行严格的数据输入、检查和编辑方法，以确保数据 尽可能的无差错和安全; 5.与PI进行定期会议，以审查招募、主题流程和小组的最新情况 匹配包括人类和啮齿动物研究; 6.设立方法和统计具体协商领域特设委员会， 工作组通常在以下领域：（i）统计遗传学（芝加哥大学的Nancy考克斯）（ii） 神经影像学和治疗结果统计学（DIG的Robert Gibbons）。

B. DATA AND STATISTICS CORE A. Primary Objectives and Responsibilities. The objectives of the Data and Statistics Core are three-fold. First, we will provide state of the art statistical consultation and support for testing scientific hypotheses of the Projects. Second, we will develop new statistical approaches to address the methodological challenges posed by novel aspects of the scientific projects. This latter effort will involve a continuation of our close reciprocal working relationships with the leaders of the Center's scientific programs as described below. Third, we will assume the data management responsibility for the human clinical studies, and will develop databases and integrate data sets across rodent and human studies as is scientifically indicated. Via our data management function, this Core will play a crucial role in maintaining integration and collaboration between the Assessment Core and individual projects, and in tracking scientific progress for the executive/administrative committee of the ACE Center. The Data and Statistics Core was vigorously involved during the conception of the proposed studies, and will continue to be a close partner in the execution and data analytic phases of the proposed research. As the projects are translational in nature, interrelated components of Core's and Projects can be addressed with full synergy both at the level of the Projects and the Center as a whole. We will pursue these broad aims by performing the following responsibilities: 1. We will work with the projects to implement state-of-the art data analytic strategies. 2. We will develop data analytic methodologies for genetic studies, for quantitative and qualitative genotype-phenotype linkage and association studies, for fMRI studies, for longitudinal studies of treatment effects, and for pharmacogenetic modeling. 3. We will work with Projects to establish the reliability and validity of new data analytic methods, and to publish new methodologies of value to the field of autism research. 4. We will work with the Assessment Core in an ongoing effort to develop more efficient, reliable and psychometrically sound clinical measures for diagnostic and treatment outcome evaluations. With the focus of our Center on the Insistence on Sameness (IS), we anticipate that an effort to develop measures in this area will be an ongoing commitment of this Core. 5. We will provide centralized management of clinical data from the assessment core, including data base development, verification, quality control procedures, and management of data access, security and archiving of the clinical data. We will also assist rodent studies in Projects 2 and 4 by way of developing data bases, and integrating quantitative observations in mouse and rat findings with clinical findings in ways that will become possible in the context of our translational research. These goals will be achieved by: 1. maintaining and developing database structures utilizing ACCESS software; 2. applying ACE-wide, standardized methods of data transfer and data management; 3. archiving all data bases on a regular basis; 4. implementing rigorous methods for data entry, checking and editing to ensure that the data are as error-free as possible and secure; 5. conducting regular meetings with Pi's to review updates on recruitment, subject flow, and group matching including both human and rodent studies; 6. establishing ad hoc committees for specific areas of methodological and statistical consultation with workgroups typically in the domains of (i) statistical genetics (Nancy Cox at University of Chicago) (ii) neuroimaging and treatment outcome statistics (Robert Gibbons at DIG).