Statistics Core

项目总结 ED-Lead正在提议一项嵌入式实用临床试验，该试验包括三个独立但潜在协同的临床试验 旨在改善对痴呆症患者的护理及其护理的干预措施 合伙人。三项干预措施--急诊护理重新设计、护士主导的电话护理和社区 辅助医疗-所有人都专注于PLWD，他们到急诊科(ED)接受护理并需要 密切关注PLWD、护理伙伴和医疗保健之间的三重接触中的护理过渡 一队。这些干预措施将分享患者水平的结果，这些结果将从联合分析中受益。建议数 随机化结构将基于多因素设计，其中ED将随机到任何 三种干预措施的组合。此设计将生成大量数据，这些数据将需要 接受评估，以评估每项干预措施的实施保真度，并评估一系列特定于干预措施 和普遍的结果。统计分析核心(SAC)将为 项目。SAC的主要职能是开发建模框架，使研究团队能够 单独评估每项干预措施，并与其他干预措施结合评估。析因设计是 这项联合研究将允许调查人员探索干预措施如何协同工作，以获得 影响比任何单一干预都要大。传统的统计推断方法通常需要非常 大样本量以执行复杂的析因实验。此外，不合理的假设 在分析析因设计时，有时需要考虑交互作用的影响。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人和联合影响 在这三种截然不同的干预措施中，从未同时进行评估。以分析框架为服务 作为基础，SAC将支持与患者级健康利用数据、培训相关的数据管理 和干预保真度、非CMS、特定于干预的患者级别结果测量和干预- 具体的执行成果衡量。SAC将监督随机化过程，以确保 参与研究的80个ED站点分布在析因设计的八个臂上 将关键站点级特征(如位置和大小)的不平衡降至最低的方式。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 在解释和展示结果方面。

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.