Etiology and Modeling Core

病因学和建模核心

• 批准号: 8425167
• 负责人: Gary Douglas Slade
• 金额: $ 20.16万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-30 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8425167
• 关键词: Address; Advisory Committees; Affect; Agreement; Biological; Blood specimen; Caring; Classification; Clinical; Cluster Analysis; Complex; Consult; Consultations; Data; Data Analyses; Data Collection; Detection; Development; Diagnosis; Environmental Exposure; Epidemiologic Methods; Equation; Etiology; Fatigue; Genetic; Genetic Markers; Individual; Joints; Label; Leukocytes; Manuals; Measures; Meta-Analysis; Methods; Modeling; Molecular; Molecular Profiling; Odds Ratio; Pain; Pain Measurement; Pain-Free; Pathway interactions; Patients; Persistent pain; Phenotype; Physiological; Plasma; Principal Component Analysis; Procedures; Process; Proteins; Protocols documentation; Research Personnel; Resources; Risk; Risk Factors; Sample Size; Specific qualifier value; Structural Models; Subgroup; Symptoms; System; Transcript; Variant; abstracting; clinical research site; data management; genetic association; genetic profiling; meetings; novel; programs; protein profiling; psychologic; psychological distress; trait

Abstract: Complex Persistent Pain Conditions: Etiology and Modeling Core. This core will support four functions relevant to subprojects and cores of this program: 1) convene an Internal Scientific Advisory Committee (ISAC) to insure consistency of data collection and analysis strategies for the program's five complex persistent pain conditions (CPPCs); 2) implement consistent study protocols and data collecfion procedures throughout the program; 3) undertake statistical analysis of pooled data from all five of the program's CPPCs; 4) consultation with project Pis in their analysis of aims specific to single CPPCs. During the first year, the ISAC will be devoted to overseeing development of study protocols and data collecfion methods. In subsequent years the ISAC will devote greater effort to refining the program's hypothesized causal model and specifying the data analysis strategy. Data collection procedures will be made consistent through creation of program-wide data management systems. Analysis of pooled data from all five of the program's CPPCs will address the program's specific aims that focus on genetic, physiologic, psychological and environmental exposures contribufing to the risk of one or more CPPCs. Conventional epidemiological methods for analysis of casecontrol studies will be compare odds of exposure between 1,500 CPPC cases and 1,230 controls who have none of the CPPCs. This sample size will allow us to identify risk factors that affect as few as 5% of subjects and that have exposure odds ratios as low as 1.6, while controlling for multiple tesfing (eg. from among 3,000 genetic markers) with an appropriate rate of false discovery. The most promising risk factors identified through these convenfional methods Will be evaluated jointly in multivariate structural equation models that isolate individual effect, interactions and pathways among causes. Investigators in the etiology and modeling care will consult with project Pis in their analysis of specific CPPCs which will follow a similar strategy of odds ratio estimation and detection of interacfion, followed by multivariate causal modeling. For these analyses, 300 cases with a specific CPPC will be contrasted with at least 1,230 controls.

抽象的： 复杂的持续性疼痛状况：病因学和建模核心。该核心将支持与该计划的子项目和核心相关的四项职能：1）召集内部科学咨询委员会（ISAC），以确保该计划的五种复杂的持续性疼痛状况（CPPC）的数据收集和分析策略的一致性； 2) 在整个过程中实施一致的研究方案和数据收集程序 程序; 3) 对来自该计划所有五个 CPPC 的汇总数据进行统计分析； 4) 与项目 Pis 协商，分析单个 CPPC 的具体目标。第一年，ISAC 将致力于监督研究方案和数据收集方法的制定。在接下来的几年中，ISAC 将投入更大的努力来完善该计划的假设因果模型并指定数据 分析策略。通过创建整个计划的数据管理系统，数据收集程序将保持一致。对该计划所有五个 CPPC 的汇总数据进行分析将解决该计划的具体目标，重点关注导致一个或多个 CPPC 风险的遗传、生理、心理和环境暴露。用于病例对照分析的常规流行病学方法 研究将比较 1,500 名 CPPC 病例和 1,230 名没有 CPPC 的对照者之间的暴露几率。这一样本量将使我们能够识别影响少至 5% 受试者且暴露比值比低至 1.6 的风险因素，同时以适当的错误率控制多重测试（例如，从 3,000 个遗传标记中进行）发现。通过以下方式确定的最有希望的风险因素 这些传统方法将在多元结构方程模型中联合评估，该模型隔离个体效应、原因之间的相互作用和路径。病因学和建模护理的研究人员将在对特定 CPPC 的分析中咨询 Pis 项目，该项目将遵循类似的比值比估计和交互检测策略，然后进行多变量因果建模。对于这些分析，300 具有特定 CPPC 的病例将与至少 1,230 个对照进行对比。