Special Scientific Procedures: Statistical Modeling Core

特殊科学程序：统计建模核心

• 批准号: 7645109
• 负责人: MICHAEL KUTNER
• 金额: $ 4.59万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7645109
• 关键词: Arts; Biological; Brain; Clinical; Collection; Data; Depressed mood; Doctor of Philosophy; Estimation Techniques; Genetic; Genetic Structures; Guidelines; Measurement; Measures; Medical; Modeling; Numbers; Patients; Procedures; Process; Purpose; Recruitment Activity; Risk; Statistical Models; Stress; Supervision; Techniques; Testing; Work; member; multidisciplinary; response

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.

招募和收集大量受试者的多学科数据的努力是 当试图利用这些信息提供实用的指导方针时， 临床医生治疗患者。为了做到这一点，我们将需要测试可能的 单独的测量之间的相互作用，如大脑功能，压力反应和潜在的 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，以及 相关的生物学和临床测量，以开始开发可靠的 抑郁症患者的治疗反应模型。我们将使用最先进的变量 选择技术，以建立和验证整体和治疗特异性的临床模型 反应，并通过使用统计技术评估其预测能力。在 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。我们将有一个 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 和监督任何治疗反应相关的分析，和一个特定的成员， 核心指定处理这些技术。