Etiology and Modeling Core

摘要： 复杂的持续性疼痛状况：病因和建模核心。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)召开一个内部科学咨询委员会(ISAC)，以确保该计划的五种复杂持续性疼痛情况(CPPC)的数据收集和分析策略的一致性；2)在整个项目中实施一致的研究方案和数据收集程序 方案；3)对所有五个方案方案的汇总数据进行统计分析；4)在分析单个方案方案的具体目标时，与项目投资机构进行协商。在第一年，ISAC将致力于监督研究方案和数据收集方法的发展。在接下来的几年里，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。将通过建立全方案的数据管理系统，使数据收集程序保持一致。对该计划所有五个CPPC的汇集数据的分析将解决该计划的具体目标，即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。病例控制分析的常规流行病学方法 研究将比较1500名CPPC患者和1230名没有CPPC的对照组的接触几率。这个样本量将使我们能够识别影响到5%的受试者的风险因素，以及暴露比低至1.6的风险因素，同时控制多个试验(例如。从3,000个遗传标记中)，具有适当的错误发现率。通过以下方式确定的最有希望的风险因素 这些常规方法将在多变量结构方程模型中进行联合评估，该模型分离个体效应、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他们对特定CPPC的分析中咨询PIS项目，该项目将遵循类似的优势比估计和相互作用检测的策略，然后进行多变量因果建模。对于这些分析，300 有特定CPPC的病例将与至少1230名对照组进行对比。

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.