Methods Core C

核心C方法

• 批准号: 10712643
• 负责人: Alistair James O'MALLEY
• 金额: $ 31.49万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712643
• 关键词: Accounting; Address; Affect; Alzheimer' s disease care; Alzheimer' s disease diagnosis; Alzheimer' s disease related dementia; Area; Assisted Living Facilities; Attention; Automation; Caring; Chronology; Collaborations; Communities; Comparative Effectiveness Research; Computer software; Computing Methodologies; Data; Data Analytics; Detection; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Diagnostic tests; Disease; Disparity; Environment; Experimental Designs; Goals; Health; Health Personnel; Health Services Accessibility; Health Status; Health system; Healthcare; Heterogeneity; Home; Hospitals; Human Resources; Inequity; Link; Machine Learning; Measurement; Measures; Medical; Medicare claim; Methods; Minority; Modeling; Network-based; Neurologist; Nursing Homes; Outcome; Output; Pathway Analysis; Patient-Focused Outcomes; Patients; Persons; Physicians; Provider; Quality of Care; Race; Research; Research Design; Risk; Risk Factors; Secure; Services; Specialist; Statistical Methods; Statistical Models; Structure; Time; Variant; Work; algorithmic bias; care outcomes; community based service; complex data; dementia care; electric impedance; health care availability; health care disparity; health disparity; health inequalities; improved; innovation; insight; interest; machine learning algorithm; medical specialties; member; mortality; network models; novel; patient population; patient responsibilities; provider networks; rurality; sex; socioeconomic disparity; statistics; synergism; trend

SUMMARY – CORE C The Statistics and Networks Methods Core (Core C) of this PPG will actively collaborate with each of the projects by helping them use statistical, network, and computational methods in their research; by advancing statistical and network methods to expand the scope of their accomplishments; and by helping them solve any unexpected problems that arise. Core C staff will collaborate with Project Leads on study designs, and will supervise Core B programmers to overcome challenges and implement methods that Core C develops inside the secure computing environment at The Dartmouth Institute, so that these can be used to analyze the patient identifiable data relevant to each project. Motivated by the recent surge of interest in health and health care for Alzheimer’s disease and related dementias (ADRD), a growing concern for health disparities and their exacerbation by algorithmic bias, and advances in statistical methods and network analysis, Core C will advance methods that will make all four projects more effective and impactful. Core C will focus on: (1) statistical models for complex data structures that expand understanding of disparities and intersectional disparities, (2) development of networks that better capture the structure of relationships linking physicians of multiple specialties through the interdependencies in their medical practice, and (3) construction of novel quality of care measures and models of network data. These methods will be applied to patients with claims- based diagnoses of ADRD or who are at risk for developing ADRD. A common new theme in this renewal PPG is the use of comparative-effectiveness research, as it overcomes bias and enhances statistical power to quantify disparities and variations by modeling hierarchically structured and other forms of dependent data and by forming inferences that are causally-defendable. Core C key personnel have made substantial progress in establishing methods that use directional relationship information from patient-physician encounters in Medicare claims to form innovative physician networks with directed edges. They will enhance this work by using specialty and services-performed data to establish even more informative networks and network-based measures of access to care and other important aspects of health care. Statistical models will also be devised that use these networks to explain additional variation in outcomes and disparities, while longitudinal models of the network itself will provide insights into trends in and the dynamic association of factors affecting ADRD diagnosis and care. These innovations will allow the PPG-supported projects to delve deeper than would be possible otherwise. Results and products (e.g., software) will be widely disseminated to research and stakeholder communities. Core C will have a major supportive and innovative impact on the research of the four projects and on the wider research communities working in ADRD, health disparities, and variations in care with a goal of improving the U.S. health system.

摘要-核心C 该PPG的统计和网络方法核心（核心C）将积极与每个 项目，帮助他们在研究中使用统计，网络和计算方法;通过推进 统计和网络方法，以扩大其成就的范围;并通过帮助他们解决任何 意想不到的问题出现。核心C工作人员将与项目负责人合作进行研究设计，并将 监督Core B程序员克服挑战并实现Core C内部开发的方法 达特茅斯研究所的安全计算环境，以便这些可以用来分析病人 与每个项目相关的可识别数据。由于最近对健康和医疗保健的兴趣激增， 阿尔茨海默病和相关痴呆症（ADRD），一个日益关注的健康差异及其 算法偏差的加剧，以及统计方法和网络分析的进步，核心C将 这些方法将使所有四个项目更加有效和有影响力。核心C将侧重于：（1） 用于复杂数据结构的统计模型，扩展了对差异和交叉的理解 差异，（2）发展网络，更好地捕捉医生之间的关系结构， 多个专业通过相互依存在他们的医疗实践，（3）建设新的 护理质量测量和网络数据模型。这些方法将适用于有索赔要求的患者- 基于ADRD的诊断或有发生ADRD风险的患者。本次更新PPG的一个共同新主题 是使用比较有效性研究，因为它克服了偏见，提高了统计能力， 通过对分层结构和其他形式的相关数据进行建模来量化差异和变化， 通过形成因果推理。核心C关键人员取得实质性进展， 建立使用来自患者-医师相遇的方向关系信息的方法， 医疗保险声称要形成具有定向优势的创新医生网络。他们将加强这项工作， 使用专业和服务执行的数据来建立信息量更大的网络， 获得保健的措施和保健的其他重要方面。还将设计统计模型 使用这些网络来解释结果和差异的额外变化，而纵向模型 网络本身将提供对影响ADRD的因素的趋势和动态关联的深入了解 诊断和护理。这些创新将使PPG支持的项目比以往更深入地研究 否则可能。结果和产品（例如，软件）将广泛传播给研究和 利益攸关方社区。核心C将对研究产生重大的支持和创新影响， 四个项目和更广泛的研究社区在ADRD工作，健康差距，以及 以改善美国医疗体系为目标。