Etiology and Modeling Core

摘要： 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功能：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）在整个研究期间实施一致的研究方案和数据收集程序。 程序; 3）对来自所有五个项目CPPC的汇总数据进行统计分析; 4）与项目PI协商，分析单个CPPC的具体目标。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。在随后的几年里，ISAC将投入更多的精力来完善该计划的假设因果模型，并指定数据 分析策略。将通过建立全方案数据管理系统，使数据收集程序保持一致。对该计划所有五种CPPC的汇总数据进行分析，将解决该计划的具体目标，重点是遗传、生理、心理和环境暴露对一种或多种CPPC风险的影响。病例对照分析的常规流行病学方法 研究将比较1，500例CPPC病例和1，230例无CPPC的对照组之间的暴露几率。该样本量将使我们能够识别影响少至5%受试者的风险因素，并且暴露比值比低至1.6，同时控制多次测试（例如，从3,000个遗传标记中选出），并具有适当的错误发现率。通过以下方法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，该模型分离了原因之间的个体效应、相互作用和途径。病因学和建模护理的研究者将在分析特定CPPC时咨询Pis项目，这将遵循相似的比值比估计和相互作用检测策略，然后进行多变量因果建模。在这些分析中，300 具有特定CPPC的病例将与至少1，230个对照进行对比。

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.