Center for Advancing Multimorbidity Science: Profiling risk and symptom expression to develop customized therapies for adults with multiple chronic conditions (CAMS)

20国集团的董事们认识到，创新的先进分析技术， 预测建模和自组织映射（SOM）对于理解复杂的 与经历多种慢性疾病的成年人相关的关系。的总体目标 集成分析核心是作为一个集中的资源，用于数据管理和高级 为中心的临床研究项目提供分析技术。通过具有专业知识的教职员工 在使用高级分析技术时，集成分析核心可作为以下方面的资源： 咨询/指导、数据收集、数据管理和数据质量控制。核心人员 在所有调查级别上支持试点PI，从制定具体假设开始， 审查研究设计，并评估测量技术的效用。辅导和指导 继续解释、介绍和公布结果，并促进 成功的R系列资助项目集成分析核心的具体目标是： 1.提供研究问题所需的多学科分析和生物统计支持，和/或 假设检验和赠款提案。 2.促进项目数据的收集、管理、传播和储存。 3.继续改进数据分析基础设施，以更好地处理 观察和结果（预测）变量。 4.创建解决基本机器学习和生物统计培训的教育计划 大型和复杂的多模式临床研究的需求。 考虑到本中心提出的问题的复杂性， 慢性病（MCC），集中的分析核心是该中心的一个基本要素， 成功集成分析核心将成为所有中心成员的资源， 先进的分析支持和生物统计咨询对规划和执行都很有用 项目考虑到目前对大数据集的收集及其重用的推动，该核心将提供一个 宝贵的数据共享资源。此外，该集成分析核心将包括对试点PI的支持 并为中心成员提供教育计划和外展服务。

The Directors of this proposed P20 recognized that innovative, advanced analytical techniques such as predictive modeling and self organizing maps (SOM) are critical to understanding the complex relationships associated with adults experiencing multiple chronic conditions. The overall goal of the Integrative Analytics Core is to serve as a centralized resource for data management and advanced analytical techniques for clinical research projects for the Center. Through faculty and staff with expertise in using advanced analytical techniques, the Integrative Analytics Core serves as a resource for consultation/guidance, data collection, data management and data quality control. Core personnel support the Pilot PIs at all levels of investigation, beginning with the formulation of specific hypotheses, reviewing of study designs, and evaluating utility of measurement techniques. Mentoring and guidance continues with the interpretation, presentation, and publication of results, and fostering development of the projects into successful R-series grants. The specific aims of the Integrative Analytics Core are: 1. Provide multidisciplinary analytics and biostatistical support needed for research questions and/or hypothesis testing and grant proposals. 2. Facilitate the collection, management, dissemination and storage of data for projects. 3. Continue to improve the data analytics infrastructure to better handle large variables of observations and outcome (predictor) variables. 4. Create educational programs that address basic machine learning and biostatistical training needs for large and complex multimodal clinical research studies. Given the complexity of the questions being asked in this Center related to individuals with multiple chronic conditions (MCC), a centralized core for analytics is an essential element to the Center's success. The Integrative Analytics Core will be a resource for all Center Members, making available advanced analytics support and biostatistical consultation useful for both planning and executing projects. Given the current push for collection of big datasets and their re-use, this Core will provide a valuable data sharing resource. In addition, this Integrative Analytics Core will include support to Pilot PIs and offer educational programs and outreach to Center Members.