Enhancing the Efficiency of Pragmatic Clinical Trials Using Administrative Data: Analysis of the STRIDE Study

使用管理数据提高实用临床试验的效率:STRIDE 研究分析

基本信息

  • 批准号:
    10588255
  • 负责人:
  • 金额:
    $ 53.88万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-03-15 至 2026-01-31
  • 项目状态:
    未结题

项目摘要

Project Summary & Abstract Pragmatic clinical trials aim to test interventions within typical healthcare settings to produce generalizable results. Successfully implementing pragmatic trials requires overcoming a number of challenges, including acquiring data as efficiently and non-intrusively as possible, so as to encourage maximum study participation at lowest cost. Administrative data are a potential solution for some pragmatic trials. These data derive from routine activities in the healthcare system, including clinical care (e.g., billing systems; use of electronic health records). With administrative data, participants can be passively followed over long time periods, potentially with decreased participant burden, decreased loss to follow-up from inability to contact a participant and decreased cost compared to alternatives (e.g., participant interview or review of medical records). All of these features could enhance both internal and external validity and reduce the overall cost of a trial. Limited empirical work exists on the comparative value of various data sources for ascertaining outcomes in pragmatic trials. We are in a unique position to the leverage the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial, a ten-site pragmatic, cluster-randomized trial focused on serious fall injury in community- dwelling older adults, to determine whether outcome ascertainment in pragmatic clinical trials could be simplified through automated data collection, without introducing significant imprecision or bias, thus reducing costs. STRIDE has multiple sources of data including multiple reference standards (adjudicated outcomes; self-reported outcomes) and two administrative data sources (fee-for-service Medicare data; administrative data from clinical trial sites). We will be able to couple currently available administrative data with newly available Medicare Advantage data to have a complete administrative picture of this almost universally Medicare eligible population. Complete data will give us the opportunity to achieve the overall goal of this research proposal, which is to develop a framework for determining whether administrative data can be used in pragmatic clinical trials in a Medicare eligible population to efficiently and accurately ascertain the primary outcome. To achieve this goal, our project has three aims: (1) develop and validate algorithms for detecting serious fall injuries from administrative data against the reference standards of STRIDE events; (2) determine the impact of the algorithms on trial findings; and (3) assess the cost efficiency (savings) of conducting the trial using administrative data.
项目摘要(&A) 务实的临床试验旨在测试典型医疗保健环境中的干预措施,以产生可推广的 结果。成功实施务实试验需要克服一些挑战,包括 尽可能高效和非侵入性地获取数据,以鼓励最大限度地参与研究 以最低的成本。行政数据是一些务实试验的潜在解决方案。这些数据源自 医疗保健系统中的常规活动,包括临床护理(例如,计费系统;使用电子健康 记录)。有了管理数据,参与者可以被被动地跟踪很长一段时间,有可能 减少了参与者负担,减少了因无法联系参与者而导致的随访损失,以及 与替代方案相比,成本更低(例如,与参与者面谈或查看医疗记录)。所有这些都是 这些功能可以提高内部和外部有效性,并降低试验的总体成本。有限 关于不同数据源在语用方面确定结果的比较价值的实证工作已经存在 审判。我们处于独特的地位,可以利用这些战略来减少伤害并培养信心 老年人(STRIDE)试验,这是一项十个地点的务实、整群随机试验,专注于社区中的严重跌伤- 居住在老年人身上,以确定在务实的临床试验中结果确定是否可以 通过自动数据收集简化,不会引入显著的不精确或偏差,从而减少 成本。STRIDE有多个数据来源,包括多个参考标准(判决结果; 自我报告结果)和两个行政数据源(服务收费医疗保险数据;行政数据 来自临床试验站点的数据)。我们将能够将当前可用的管理数据与新的 可用的Medicare Advantage数据可以全面了解这一点 符合联邦医疗保险条件的人口。完整的数据将使我们有机会实现这一总体目标 研究提案,这是为了制定一个框架,以确定行政数据是否可以用于 在符合医疗保险条件的人群中进行实用的临床试验,以有效和准确地确定初级 结果。为了实现这一目标,我们的项目有三个目标:(1)开发和验证检测算法 行政数据严重坠落伤违反跨步项目参考标准;(2)确定 算法对试验结果的影响;以及(3)评估进行试验的成本效益(节省) 使用管理数据。

项目成果

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Denise Esserman其他文献

Denise Esserman的其他文献

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{{ truncateString('Denise Esserman', 18)}}的其他基金

Enhancing the Efficiency of Pragmatic Clinical Trials Using Administrative Data: Analysis of the STRIDE Study
使用管理数据提高实用临床试验的效率:STRIDE 研究分析
  • 批准号:
    10365009
  • 财政年份:
    2022
  • 资助金额:
    $ 53.88万
  • 项目类别:
Core-002-Biostatistical Core
Core-002-生物统计核心
  • 批准号:
    10728897
  • 财政年份:
    2002
  • 资助金额:
    $ 53.88万
  • 项目类别:

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