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

项目总结/摘要 急性呼吸窘迫综合征（ARDS）是一种常见的、毁灭性的急性呼吸衰竭原因。 美国每年有20万例ARDS病例（全球250 - 500万例），导致6万例死亡 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）中，只有两种干预措施-低潮气量通气和俯卧位- 使用传统的频率论、零假设、基于p值的试验设计明确改善了结局 和分析研究小组认为，在这一框架下评估数据可能会忽视信息试验， 数据和延迟或阻碍有希望的疗法的识别，特别是当p值刚好低于 0.05阈值，这已经发生在几个主要的ARDS试验中。作为一种替代方法， 为了最大限度地从RCT中获得临床见解，该团队将重新分析29个国际和NHLBI资助的 使用贝叶斯因果推断和机器学习招募超过15，000人的ARDS RCT 他们开发和验证的方法。他们将研究的大多数疗法要么成本低，要么容易 实施的实践，因此具有高影响力的潜力（例如，呼吸机设置，液体管理， 皮质类固醇、他汀类、β-激动剂、维生素D）。在目标1中，他们将不使用统计显著性， 量化有益治疗效果的概率及其可能的程度。也就是说， 预先规定的p值来确定干预是否至少具有假设的死亡率效益， 将推导出给定治疗与临床相关绝对死亡率降低相关的概率 至少2%、4%和6%。他们将用无信息的贝叶斯“先验”来检查每一种干预， 采用标准化和荟萃分析得出的先验知识，以减少主观性并询问临床疗效 在伤害和利益的可能性的范围内。在目标2中，他们将使用贝叶斯加法回归 他们开发了BART制剂，以了解哪些ARDS患者类型最有可能受益 或因治疗而受到伤害，即，所谓的“治疗效果的异质性”（HTE）。与之前的HTE不同 在ARDS的研究中，他们的方法并不关注一个接一个的二元特征分裂，而是可以 识别复杂、多变量、非线性治疗效果修正。目标2a将侧重于死亡率， 不良事件。目标2b将应用一种新的BART变体来识别结局中的HTE，例如呼吸机 观察因死亡而缩短的持续时间或住院时间。通过估计因果效应， 他们的新方法避免了与先前方法相关的偏差， 从而能够准确识别具有临床意义的亚组。目标3侧重于开发和 传播免费的云软件以支持未来的ARDS试验。这项工作有望改善 通过识别真正有益的治疗方法，从过去和未来的ARDS RCT中获得的知识的价值， 告知这些疗法如何针对这种高死亡率，高发病率的综合征进行个性化定制。

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.