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

这一提议的P20的董事们认识到，创新的先进分析技术，如 预测建模和自组织映射(SOM)对于理解复杂性至关重要 与经历多种慢性病的成年人有关的关系。该计划的总体目标 集成分析的核心是作为数据管理的集中化资源和高级 该中心临床研究项目的分析技术。通过拥有专业知识的教职员工 在使用高级分析技术时，综合分析核心可作为以下资源 咨询/指导、数据收集、数据管理和数据质量控制。核心人员 支持各级调查的试点绩效指标，从提出具体假设开始， 回顾研究设计，评估测量技术的实用性。指导和指导 继续解释、演示和发布结果，并促进 将这些项目转化为成功的R系列赠款。综合分析核心的具体目标是： 1.提供研究问题和/或研究问题所需的多学科分析和生物统计学支持 假设检验和拨款建议。 2.便利项目数据的收集、管理、传播和存储。 3.继续改善数据分析基础设施，以更好地处理 观察和结果(预测)变量。 4.创建针对基本机器学习和生物统计培训的教育项目 对大型和复杂的多模式临床研究的需求。 鉴于本中心提出的问题的复杂性，这些问题与患有多项疾病的个人有关 慢性疾病(MCC)是一个集中的分析核心，是该中心 成功。综合分析核心将成为所有中心成员的资源，提供 有助于规划和执行的高级分析支持和生物统计咨询 项目。鉴于目前对大数据集收集和重用的推动，该核心将提供 宝贵的数据共享资源。此外，此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项目和外展服务。

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.