Special Scientific Procedures: Statistical Modeling Core

招募和收集关于大量学科的多学科数据的努力是 在尝试使用此信息为以下方面提供实用指南时最大的好处 临床医生给病人治病。为了实现这一点，我们需要测试可能的 脑功能、应激反应和潜在反应等不同指标之间的相互作用 遗传结构。这个核心的目的将是使用模型构建的组合 技术，类似于医疗风险模型中使用的技术，并全面收集 相关的生物学和临床措施，以开始开发可靠的进程 抑郁症患者的治疗反应模式。我们将使用最先进的变量 选择技术来构建和验证针对总体和特定治疗的临床模型 通过使用统计技术，评估其预测能力。在……里面 此外，我们将纳入遗传信息，这将需要专门的估计 技术，以模拟与治疗反应的关系。因此，我们将拥有一个 统计遗传学家(迈克尔·爱泼斯坦博士)与核心密切合作，提供专业知识 和监督任何与治疗反应相关的分析，以及 指定用于处理这些技术的核心。

The effort to recruit and collect multidisciplinary data on a large number of subjects is of most benefit when attempting to use this information to provide practical guidelines for clinicians treating patients. In order to accomplish this, we will need to test the possible interactions among separate measures such as brain function, stress reactivity and underlying genetic structure. The purpose of this core will be to use a combination of model building techniques, similar to those used in models of medical risk, and a comprehensive collection of relevant biological and clinical measurements to begin the process of developing reliable models of treatment response for depressed patients. We will use state-of-the-art variable selection techniques to build and validate a clinical model for overall and treatment-specific response, and evaluate its predictive ability through the use of statistical techniques. In addition, we will be incorporating genetic information, which will require specialized estimation techniques in order to model the relationship with treatment response. Thus we will have a statistical geneticist (Michael Epstein, PhD) working closely with the core to provide expertise and supervision for any treatment response related analyses, and a specific member of the core designated for handling those techniques.