Statistics Core

统计核心

• 批准号: 10709337
• 负责人: Keith Goldfeld
• 金额: $ 61.14万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10709337
• 关键词: Accident and Emergency department; Alzheimer' s Disease; Attention; Bayesian Modeling; Biometry; Caring; Characteristics; Cluster randomized trial; Collaborations; Collection; Communities; Complex; Computing Methodologies; Consult; Consultations; Data; Data Analyses; Databases; Dementia; Elements; Emergency Care; Ensure; Equilibrium; Foundations; Goals; Graph; Health Personnel; Health Policy; Health Status; Health system; Healthcare Systems; Individual; Intervention; Joints; Lead; Link; Location; Manuscripts; Measures; Medical Care Team; Methodology; Modeling; Monitor; Nurses; Outcome; Outcome Measure; Paramedical Personnel; Patients; Persons; Policy Maker; Pragmatic clinical trial; Process; Randomized; Reporting; Research; Research Personnel; Sample Size; Scheme; Site; Statistical Data Interpretation; Statistical Methods; Structure; System; Telephone; Training; Validity and Reliability; Vulnerable Populations; Work; analytical method; arm; data exploration; data management; dementia care; design; experimental study; implementation evaluation; implementation fidelity; implementation outcomes; improved; statistics; tool; trial design

PROJECT SUMMARY ED-LEAD is proposing an embedded pragmatic clinical trial of three independent yet potentially synergistic interventions all targeted at improving the care of Persons Living with Dementia (PLWD) and their care partners. The three interventions – emergency care redesign, nurse-led telephonic care, and community paramedicine – all focus on PLWD who present to the Emergency Department (ED) for care and the need for careful attention to care transitions in the triadic encounter between the PLWD, care partner, and healthcare team. These interventions will share patient-level outcomes that will benefit from a joint analysis. The proposed randomization structure will be based on a multifactorial design, where EDs will be randomized to any combination of the three interventions. This design will generate substantial quantities of data that will need to be evaluated to assess implementation fidelity of each intervention and assess a range of intervention-specific and universal outcomes. The Statistical Analysis Core (SAC) will provide biostatistical expertise for the overall project. The SAC’s key function is to develop the modeling framework that will enable the study team to evaluate each intervention individually and in combination with others. The factorial design is a key element of the joint study that will allow the investigators to explore how the interventions might work together to have a greater impact than any single intervention. Traditional methods of statistical inference typically require very large sample sizes to perform complex factorial experiments. Furthermore, unreasonable assumptions regarding the absence of interaction effects are sometimes required in analyzing a factorial design. We have developed a Bayesian modeling approach that will enable us to present the results in a way that will allow health care providers, health care systems, and health policy makers to assess the individual and joint impacts of these three very different interventions never evaluated simultaneously. With the analytic framework serving as a foundation, the SAC will support data management related to patient-level health utilization data, training and intervention fidelity, non-CMS, intervention-specific patient-level outcome measurement, and intervention- specific implementation outcome measurement. The SAC will oversee the randomization process to ensure that the 80 ED sites participating in the study are distributed across the eight arms of the factorial design in a way that minimizes imbalance of key site-level characteristics, such as location and size. The SAC will perform statistical analyses and data exploration using appropriate statistical and computing methodologies, and assist in interpreting and presenting results.

项目摘要 ED-LEAD提出了一个嵌入式实用的临床试验，三个独立的，但潜在的协同作用 所有干预措施都旨在改善对痴呆症患者及其护理 伙伴三种干预措施-急诊护理重新设计、护士主导的电话护理和社区 护理人员-所有的重点都是到急诊室（艾德）寻求护理和需要 密切关注PLWD、护理伙伴和医疗保健之间三方接触中的护理过渡 团队这些干预措施将分享患者层面的结果，这些结果将从联合分析中受益。拟议 随机化结构将基于多因素设计，其中ED将随机分配至任何 三种干预措施的结合。这种设计将产生大量的数据， 进行评价，以评估每项干预措施的实施保真度，并评估一系列具体干预措施 和普遍的结果。统计分析核心（SAC）将为总体 项目SAC的主要功能是开发建模框架，使研究团队能够 单独评估每项干预措施，并与其他干预措施相结合。析因设计是 这项联合研究将使研究人员能够探索干预措施如何共同发挥作用， 比任何单一的干预措施都要有效。传统的统计推断方法通常需要非常 大样本量进行复杂的析因实验。此外，不合理的假设 在分析析因设计时，有时需要考虑无交互作用效应。我们有 开发了一种贝叶斯建模方法，使我们能够以一种允许 卫生保健提供者、卫生保健系统和卫生政策制定者评估个体和联合影响 这三种截然不同的干预措施从未同时进行评估。分析框架服务于 作为一个基础，SAC将支持与患者级卫生利用数据有关的数据管理，培训 和干预保真度，非CMS，干预特定的患者水平的结果测量，和干预- 具体执行成果衡量。SAC将监督随机化过程，以确保 参与研究的80个艾德研究中心分布在析因设计的8个组中， 最大限度地减少关键站点级别特征（如位置和大小）的不平衡。SAC将执行 使用适当的统计和计算方法进行统计分析和数据探索，并协助 解释和展示结果。