Statistical Analysis Core

总结 在这种情况下，产生多层次行为、基因型和成像数据的财富所带来的好处是， PPG取决于这些数据分析的质量。统计分析（SA）核心将形成一个中心 在统计和数据分析方面为人口统计小组提供专门知识和支持，其使命是加强科学研究， 确保数据的最佳使用。由于数据分析的最佳结果是通过 结合数据分析的专业知识和相关主题的专业知识，SA核心已经 旨在将耶鲁大学统计系的统计专家与精选数据汇集在一起- 从项目1-5谁是熟悉的科学导向的人员。这项合作将 开发和应用最强大和最具启发性的分析方法，保持严格的统计有效性 而不人为地限制方法的复杂性。各个项目将进行分析， 实现他们的具体目标，SA核心将根据需要提供协助。SA核心还将负责 使用来自多个项目的数据进行分析，以解决跨越多个项目的综合目标。 队列年龄和数据类型在SA核心描述中详细介绍了几个综合目标。比如说， 我们将使用产前和新生儿时期的纵向fMRI连接测量， 项目1和项目4预测自闭症的严重程度，并预测对新的社会价值训练的反应 项目5中提出的方案。我们将调整来自项目2的大脑连接网络， 与学龄儿童的持续关注有关，以研究项目1中年龄太小， 选择性注意力要有效地衡量。并将诱导的神经生物学特征 多能干细胞从项目3到fMRI网络连接和措施的注意力和自闭症， 小学生项目2在通过仔细应用经典的 方法，如方差分析，回归和混合效应模型，SA核心将寻求应用更现代， 统计理论和方法的复杂发展，以改进分析。详见 在SA核心描述中，成员被选为在统计方面具有独特和互补的专业知识， 预计对项目和综合目标具有重要意义的领域，如高维统计 （如本PPG中所述，适用于测量每位参与者的许多变量），维度 减少，统计计算，纵向分析和网络模型（这将适用于两个大脑 连接网络和遗传网络）。SA核心的附带好处是招聘 周博士和Negahban博士，在统计研究的最高水平工作的学者， 自闭症研究我们还预计耶鲁统计学研究生的参与会增加 系，这是目前进入一个充满活力的扩张期，在自闭症研究的耶鲁儿童 研究中心。 1

Summary The benefits to come from producing the wealth of multi-level behavioral, genotypic, and imaging data in this PPG depend on the quality of the analyses of these data. The Statistical Analysis (SA) Core will form a center of expertise and support in Statistics and data analysis for the PPG, with a mission of enhancing scientific progress by ensuring optimal use of data. As optimal results in data analysis are achieved through a combination of expertise in data analysis with expertise in relevant subject matter, the SA Core has been designed to bring statistical experts from the faculty of the Yale Statistics Department together with select data- oriented personnel from Projects 1-5 who are intimately familiar with the science. This collaboration will develop and apply the most powerful and revealing analytic methods, maintaining rigorous statistical validity without artificially limiting the sophistication of the methodology. The individual Projects will perform analyses to achieve their specific aims and the SA Core will assist as needed. The SA Core will also be responsible for performing analyses that use data from more than one project to address integrative aims that span across cohorts, ages, and data types. Several integrative aims are detailed in the SA Core description. For example, we will use longitudinal fMRI connectivity measurements made in the prenatal and neonatal periods from Project 1 and Project 4 to predict autism severity and also to predict response to a novel social value training protocol proposed in Project 5. We will adapt brain connectivity networks from Project 2 derived as maximally associated with sustained attention in school-age children to study neonates in project 1 who are too young for selective attention to be measured effectively. And we will relate neurobiological characteristics of induced pluripotent stem cells from Project 3 to fMRI network connectivity and measures of attention and autism in school age from Project 2. After establishing baseline analyses through careful applications of classical methods such as ANOVA, regression, and mixed-effects models, the SA Core will seek to apply more modern, sophisticated developments in statistical theory and methodology to develop improved analyses. As detailed in the SA Core description, members were chosen to have distinct and complementary expertise in statistical areas expected to be of importance to the projects and integrative aims, such as high dimensional statistics (which applies whenever many variables are measured for each participant as in this PPG), dimension reduction, statistical computing, longitudinal analysis, and network models (which will apply to both brain connectivity networks and genetic networks in this PPG). A collateral benefit of the SA Core is the recruitment of Dr. Zhou and Dr. Negahban, scholars working at the highest levels of statistical research, to the field of autism research. We also anticipate increased participation by graduate students from the Yale Statistics Department, which is currently entering a vibrant period of expansion, in the autism research of the Yale Child Study Center. 1