Improving the statistical design and analysis of randomized controlled trials of delirium prevention and treatment for critically ill older adults

改进危重老年人谵妄预防和治疗随机对照试验的统计设计和分析

基本信息

  • 批准号:
    10064600
  • 负责人:
  • 金额:
    $ 35.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-02-15 至 2022-11-30
  • 项目状态:
    已结题

项目摘要

Project Summary/Abstract Among critically ill older adults receiving care in an intensive care unit (ICU), delirium is very common (>70%) and strongly associated with long-term cognitive impairment similar to Alzheimer's Disease and Related Dementias (ADRD). With growing recognition of this major problem, there is an increasing number of randomized controlled trials (RCTs) evaluating preventative and therapeutic interventions for delirium among critically ill older adults. Efforts are underway to improve the scientific rigor of such trials; specifically, a NIH/NIA NIDUS collaborative (R24AG054259) and a Canadian CIHR-funded project that aim to harmonize use of outcome measurement instruments to improve comparability and consistency across delirium RCTs. However, despite these efforts, guidance regarding delirium endpoint definition and appropriate statistical methods for evaluating treatment effects in delirium RCTs is lacking. Defining and evaluating a delirium endpoint is especially challenging for older adults in the ICU as delirium status can vary during the follow-up period (e.g. 28 days) and measurement may be terminated by patient discharge or death, referred to as “competing events” in the statistical literature. Both discharge and death are correlated with the risk of delirium, but likely in opposite directions, and may be affected by the intervention, further complicating evaluation of delirium endpoints. Thus, our overall objective is to improve the design and statistical analysis of preventative and therapeutic delirium RCTs in critically ill older adults. To achieve this objective, we will conduct a systematic review of delirium endpoint definitions and statistical analysis methods, followed by a rigorous evaluation of their statistical performance (e.g., false positive rate (Type I error) and statistical power), using simulation studies based on real data from 6 diverse RCT exemplars and 2 large clinical cohorts (Aim 1). Joint models, that include survival models for both recurring delirium and a single competing event, have recently been applied in two prominent RCTs of pharmacological interventions for delirium in the ICU (Aim 2). In this proposal, novel extensions of these joint models will be developed that better mimic key features of delirium among critically ill older adults by including a recurrent event survival model for delirium, as well as allowing two survival models, one for each competing event. Thereafter, current and novel extensions of the joint models will be compared to the endpoint definitions and statistical analysis methods identified in the preceding systematic review, using simulation studies (Aim 3). Based on these findings, we will make recommendations for the design and statistical analysis of delirium RCTs, accounting for the possibility that discharge and death may be affected by the study intervention. We will disseminate these novel joint models, in addition to our findings and recommendations, through publications, open source statistical software, a stand-alone statistical application, an established ICU research methodology dissemination infrastructure, and aging, delirium, and statistical professional societies.
项目总结/摘要 在重症监护室(ICU)接受护理的危重老年人中,谵妄非常常见(>70%) 并与类似于阿尔茨海默病的长期认知障碍密切相关, 痴呆症(ADRD)。随着人们越来越认识到这一重大问题, 随机对照试验(RCT)评价了在2011年至2012年期间 老年重症患者。目前正在努力提高此类试验的科学严谨性;具体而言, NIH/NIA NIDUS协作(R24 AG 054259)和加拿大CIHR资助的项目,旨在协调使用 结果测量工具,以提高谵妄RCT的可比性和一致性。 然而,尽管做出了这些努力,关于谵妄终点定义和适当统计学的指南 目前尚缺乏评价谵妄随机对照试验治疗效果的方法。定义和评估谵妄 终点对于ICU中的老年人尤其具有挑战性,因为随访期间谵妄状态可能会有所不同 周期(例如28天)和测量可能因患者出院或死亡而终止, “竞争事件”在统计学文献中。出院和死亡都与以下风险相关: 谵妄,但可能在相反的方向,并可能受到干预,进一步复杂化 谵妄终点的评价。因此,我们的总体目标是改进设计和统计 危重老年人谵妄预防性和治疗性随机对照试验分析实现这一 目的,我们将对谵妄终点定义和统计分析方法进行系统回顾, 然后对其统计性能进行严格评估(例如,假阳性率(I类错误)和 统计功效),使用基于6个不同RCT样本和2个大型RCT样本的真实的数据的模拟研究 临床队列(目标1)。联合模型,包括复发性谵妄和单一 竞争事件,最近已应用于两个突出的药物干预RCT, ICU中的谵妄(目标2)。在这个提议中,将开发这些联合模型的新扩展, 通过纳入复发事件生存率,更好地模拟危重老年人中谵妄的关键特征 谵妄模型,以及允许两个生存模型,每个竞争事件一个。此后,目前 将关节模型的新扩展与终点定义和统计分析进行比较 在前面的系统评价中确定的方法,使用模拟研究(目标3)。基于这些 研究结果,我们将为谵妄RCT的设计和统计分析提出建议, 出院和死亡可能受到研究干预的影响。我们将传播这些 新颖的联合模型,除了我们的发现和建议,通过出版物,开源 统计软件,独立的统计应用程序,既定的ICU研究方法 传播基础设施,老龄化,谵妄,统计专业协会。

项目成果

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Elizabeth Colantuoni其他文献

Elizabeth Colantuoni的其他文献

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{{ truncateString('Elizabeth Colantuoni', 18)}}的其他基金

Improving Hospital Efficiency: Predicting Post-Acute Care Facility Placement Using Machine Learning and Patient Mobility Scores from the Electronic Medical Record
提高医院效率:使用机器学习和电子病历中的患者流动性评分来预测急性后护理设施的安置
  • 批准号:
    10056338
  • 财政年份:
    2020
  • 资助金额:
    $ 35.5万
  • 项目类别:
Improving the statistical design and analysis of randomized controlled trials of delirium prevention and treatment for critically ill older adults
改进危重老年人谵妄预防和治疗随机对照试验的统计设计和分析
  • 批准号:
    10356807
  • 财政年份:
    2019
  • 资助金额:
    $ 35.5万
  • 项目类别:

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