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）是急性呼吸衰竭的常见且毁灭性的原因。美国有200,000例年度ARDS病例（全球2500万），造成60,000人死亡以及生存之间的巨大身体，认知和社会心理发病率。但是，dospite超过200 随机临床试验（RCT），只有两种干预措施 - 低潮汐通风和易于定位 - 使用传统的频率主义者，零假设，基于p值的试验设计，已最终改善了结果和分析。研究小组涉及在此框架中评估数据可能会忽略内容丰富的试验数据和延迟或阻碍了承诺疗法的识别，尤其是当p值仅落在 0.05阈值，发生在几项主要的ARDS试验中。作为替代方法论方法最大化从RCT中获得的临床见解，该团队将重新分析29国际和NHLBI资助使用贝叶斯因果推理和机器学习招募了15,000多名个人的ARDS RCT 他们开发和验证的方法。他们将检查的大多数疗法是低成本或容易实施实践，因此具有高影响力（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AIM 1中，而不是使用统计意义，他们将量化有益治疗效果的可能性及其有问题的幅度。也就是说，而不是使用预先指定的p值确定干预措施是否至少具有假设的死亡率，它们将得出给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％。他们将使用非信息贝叶斯的“先验”检查每种干预措施，然后具有标准化和荟萃分析衍生的先验，以降低主观性并询问临床效率跨越危害和利益潜力。在AIM 2中，他们将使用贝叶斯添加剂回归他们开发的树木（BART）公式以了解哪种ARDS患者类型最有可能受益从或受到一种疗法的伤害，即所谓的“治疗效果异质性”（HTE）。与先前的HTE不同 ARDS的研究，他们的方法并不集中于一对一的，特征的二元分裂，而是可以确定复杂的，多变量的非线性治疗效果修饰。 AIM 2A将专注于死亡率和不利事件。 AIM 2B将应用新颖的BART变体，以识别诸如呼吸机等结果中的HTE 持续时间或住院，其观察被死亡截断。通过估计对这些因果的影响在始终活跃者中的结果，他们的新方法避免了与先前方法相关的偏见，能够准确识别临床上有意义的亚组。 AIM 3专注于发展和传播基于云的免费软件，以支持未来的ARDS试验。这项工作有望改善通过确定真正有益的治疗和告知这些疗法如何针对这种高病态，高病症综合征的单独定制。