5/6 COMpAAAS U24: Resource in Informatics and Biostatistics (RIB)

NIAAA资助的财团的总体目标是改善艾滋病毒/艾滋病、酗酒、老龄化和多种疾病的结果。 物质使用(COMpAAAS)是建立和传播优化艾滋病毒+护理所需的证据 体验酒精和相关物质使用造成的医疗伤害，通过协调、整合和 外部验证的观察性、运筹性研究建模和干预研究。组合和 整合了信息学、生物统计学和流行病学的免费专业知识，我们提出了U24 信息学和生物统计(RIB)的资源，用于支持和通知COMpAAAS的其他组成部分。这 资源将解决在分析中最大限度地利用权力和最大限度地减少偏差所需的复杂挑战 解决整个财团的问题。支持的高级信息学方法包括自然语言 处理(NLP)、本体、数据库和临床决策支持，以及重要数据管理的应用 用于安全数据收集、存储、注释、检索和集成的工具。高级流行病学和 统计方法包括时间更新的曝光技术、多重归罪、倾向评分 技术、测量误差修正和竞争风险回归。常规的，但必不可少的统计数据 方法包括COX比例风险、Logistic和线性回归、拟合优度诊断法和 一致性/准确性指标(kappa、灵敏度、特异度等)。肋骨将进一步利用 观察性和干预性研究、模拟模型以及协调良好的核心和 COMpAAAS工作组采用先进的信息学和生物统计技术为特定的 来自多个来源的大规模纵向数据的挑战，包括电子健康记录(EHR)、 临床干预、患者自我报告和组织信息库。我们的具体目标是提供统计数据 2)COMpAAAS的信息学专业知识，以最大限度地发挥科学影响。为了实现这一目标，我们将加强 干预研究的设计、招募和后续行动，支持适当的数据设计和执行 分析和跨队列协作，提供高级统计方法和酒精估计 根据Vacs调查数据进行OR建模并确定性/性别少数群体(CHAMP)的模式。 此外，我们将加强联盟基于网络的实验室(WBL门户网站)的信息基础设施，以 支持正在进行的研究设计、数据收集和管理、干预措施的开发和测试 例如临床决策支持和电子健康工具，并增强对文本数据分析的支持。

The overall aim of the NIAAA-funded Consortium to improve OutcoMes in hiv/Aids, Alcohol, Aging, & multi- Substance use (COMpAAAS) is to build and disseminate the evidence needed to optimize care for HIV+ experiencing medical harm from alcohol and related substance use, through coordinated, integrated, and externally validated observational, operations research modeling, and intervention studies. Combining and integrating the complimentary expertise of informatics, biostatistics and epidemiology, we propose a U24 Resource for Informatics and Biostatistics (RIB) to support and inform the other COMpAAAS components. This resource will address the complex challenges required to maximize power and minimize bias in analyses addressing consortium-wide questions. Advanced informatics methods supported include natural language processing (NLP), ontologies, database and clinical decision support, and application of vital data management tools for secure data collection, storage, annotation, retrieval, and integration. Advanced epidemiological and statistical methods include time-updated exposure techniques, multiple imputation, propensity score techniques, measurement error correction, and competing risks regression. Routine, but essential, statistical methods include Cox proportional hazards, logistic and linear regression, goodness of fit diagnostics, and agreement/accuracy metrics (kappa, sensitivity, specificity, etc.). The RIB will further leverage the observational and interventional studies, simulation models, and well-coordinated network of cores and workgroups of COMpAAAS with advanced informatics and biostatistical techniques tailored to the particular challenges of large scale, longitudinal data from multiple sources including electronic health records (EHR), clinical interventions, patient self-report, and tissue repositories. Our specific aims are to provide 1) statistical and 2) informatics expertise for COMpAAAS to maximize scientific impact. To accomplish this, we will enhance the design, recruitment, and follow-up of intervention studies, support appropriate design and execution of data analyses and cross cohort collaborations, provide advanced statistical methods plus estimates of alcohol patterns for OR modeling and identify sexual/gender minority populations (CHAMP) from VACS survey data. In addition we will enhance the Consortium Web-Based Laboratory (WBL Portal) informatics infrastructure to support ongoing research design, data collection and management, development and testing of interventions such as clinical decision support and eHealth tools and enhance support for the analysis of textual data.