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Advancing the design, analysis, and interpretation of acute respiratory distress syndrome trials using modern statistical tools

使用现代统计工具推进急性呼吸窘迫综合征试验的设计、分析和解释

基本信息

批准号：
10633978
负责人：
Michael Oscar Harhay
金额：
$ 77.97万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10633978
关键词：
Acute Respiratory Distress Syndrome Acute respiratory failure Address Adrenal Cortex Hormones Adverse event Advocate Agonist Bayesian Method Bayesian Modeling Belief COVID-19 Cessation of life Characteristics Clinical Clinical Practice Guideline Clinical effectiveness Cognitive Complex Computer software Data Detection Disease Epidemiology Ergocalciferols Formulation Funding Future Heterogeneity Hospital Mortality Individual International Intervention Intuition Investigation Knowledge Learning Length of Stay Liquid substance Meta-Analysis Methodology Methods Modernization Modification Morbidity - disease rate National Heart, Lung, and Blood Institute Neuromuscular Blocking Agents Outcome Output Patients Pneumonia Probability Prone Position Publishing Recommendation Research Research Personnel Respiratory Failure Sepsis Societies Specific qualifier value Standardization Statistical Methods Subgroup Survivors Syndrome Techniques Testing Tidal Volume Treatment Efficacy United States National Institutes of Health Variant Ventilator Vitamin D Work adjudication clinical efficacy clinical trial enrollment clinically relevant cloud based cost design evidence base falls improved improved outcome innovation insight machine learning method mortality novel novel strategies participant enrollment patient subsets prevent psychosocial randomized, clinical trials regression trees respiratory virus tool treatment effect treatment response trial design ventilation web-based tool

项目摘要

PROJECT SUMMARY/ABSTRACT Acute respiratory distress syndrome (ARDS) is a common and devastating cause of acute respiratory failure. There are 200,000 annual ARDS cases in the U.S. (2.5-5 million globally), which account for 60,000 deaths and enormous physical, cognitive, and psychosocial morbidity among survivors. Yet, despite more than 200 randomized clinical trials (RCTs), only two interventions – low-tidal-volume ventilation and prone positioning – have definitively improved outcomes using a traditional frequentist, null hypothesis, p-value-based trial design and analysis. The research team contends that assessing data in this framework may overlook informative trial data and delay or thwart the identification of promising therapies, especially when p-values fall just short of the 0.05 threshold, which has occurred in several major ARDS trials. As an alternative methodological approach to maximize the clinical insight gained from RCTs, the team will reanalyze 29 international and NHLBI-funded ARDS RCTs that enrolled more than 15,000 individuals using Bayesian causal inference and machine learning methods they have developed and validated. Most therapies they will examine are either low-cost or easily implemented practices and thus have the potential for high impact (e.g., ventilator settings, fluid management, corticosteroids, statins, beta-agonists, vitamin D). In Aim 1, instead of using statistical significance, they will quantify the probability of a beneficial treatment effect and its probable magnitude. That is, instead of using a pre-specified p-value to determine whether an intervention has at least the hypothesized mortality benefit, they will derive the probability that a given therapy is associated with clinically relevant absolute mortality reductions of at least 2%, 4%, and 6%. They will examine each intervention with noninformative Bayesian ‘priors’ and then with standardized and meta-analysis-derived priors to reduce subjectivity and interrogate clinical efficacy across the spectrum of harm and benefit possibilities. In Aim 2, they will use Bayesian Additive Regression Trees (BART) formulations they developed to understand which ARDS patient types are most likely to benefit from, or be harmed by, a therapy, i.e., so-called ‘heterogeneity of treatment effect’ (HTE). Unlike prior HTE research in ARDS, their approach does not focus on one-by-one, binary splits of characteristics but rather can identify complex, multivariable, nonlinear treatment effect modification. Aim 2a will focus on mortality and adverse events. Aim 2b will apply a novel BART variation to identify HTE in outcomes such as ventilator duration or hospital stay whose observation is truncated by death. By estimating causal effects on these outcomes among always-survivors, their new method avoids biases associated with prior approaches, enabling accurate identification of clinically meaningful subgroups. Aim 3 focuses on developing and disseminating free, cloud-based software to support future ARDS trials. This work promises to improve the value of the knowledge gained from past and future ARDS RCTs by identifying truly beneficial treatments and informing how these therapies can be individually tailored for this high-mortality, high-morbidity syndrome.

项目摘要/摘要急性呼吸窘迫综合征(ARDS)是导致急性呼吸衰竭的一种常见且具有破坏性的原因。美国每年有20万例急性呼吸窘迫综合征(ARDS)病例(全球250-500万例)，其中6万人死亡以及幸存者中巨大的身体、认知和心理社会发病率。然而，尽管有200多个随机临床试验(RCT)，只有两种干预措施--低潮气量通气法和俯卧位-- 使用传统的基于频率、零假设、基于p值的试验设计，结果确实得到了改善和分析。研究小组争辩说，这个框架中的评估数据可能会忽视信息丰富的试验数据和延迟或阻碍了有希望的疗法的识别，特别是当p值略低于 0.05的阈值，这已经在几个主要的ARDS试验中发生。作为一种替代的方法论方法最大化从随机对照试验中获得的临床洞察力，该团队将重新分析29个国际和NHLBI资助的项目使用贝叶斯因果推理和机器学习招募了15,000多人的ARDS随机对照试验他们开发和验证的方法。他们将检查的大多数疗法要么成本低，要么很容易所实施的实践因此具有高影响的潜力(例如，呼吸机设置，流体管理，皮质类固醇、他汀类药物、β-激动剂、维生素D)。在目标1中，他们将不使用统计意义，而是量化有益的治疗效果的概率及其可能的大小。也就是说，不是使用预先指定的p值以确定干预是否至少具有假设的死亡率收益，它们将得出一种特定疗法与临床相关的绝对死亡率降低相关的概率至少2%、4%和6%。他们将用非信息性的贝叶斯“先验”来检查每一项干预，然后使用标准化和Meta分析派生的先验，以减少主观性并询问临床疗效各种危害和益处的可能性。在目标2中，他们将使用贝叶斯加法回归他们开发了Trees(BART)公式，以了解哪些ARDS患者类型最有可能受益来自治疗或被治疗伤害，即所谓的治疗效果的异质性(HTE)。与以前的HTE不同在ARDS的研究中，他们的方法不关注一个接一个的二进制特征分裂，而是可以识别复杂、多变量、非线性的治疗效果修正。目标2a将重点关注死亡率和不良事件。Aim 2b将应用一种新的BART变异来确定呼吸机等结局中的HTE 观察因死亡而被截断的时间或住院时间。通过估计这些因素的因果影响在总是幸存者的结果中，他们的新方法避免了与先前方法相关的偏见，能够准确识别具有临床意义的亚组。目标3侧重于开发和传播免费的基于云的软件，以支持未来的ARDS试验。这项工作有望改善从过去和未来ARDS随机对照试验中获得的知识的价值，通过确定真正有益的治疗和告知如何为这种高死亡率、高发病率的综合征量身定做这些疗法。