Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street, 600 Scaife Hall, Pittsburgh, PA, 15261, USA.
Crit Care. 2023 Nov 8;27(1):432. doi: 10.1186/s13054-023-04717-x.
Given the success of recent platform trials for COVID-19, Bayesian statistical methods have become an option for complex, heterogenous syndromes like sepsis. However, study design will require careful consideration of how statistical power varies using Bayesian methods across different choices for how historical data are incorporated through a prior distribution and how the analysis is ultimately conducted. Our objective with the current analysis is to assess how different uses of historical data through a prior distribution, and type of analysis influence results of a proposed trial that will be analyzed using Bayesian statistical methods.
We conducted a simulation study incorporating historical data from a published multicenter, randomized clinical trial in the US and Canada of polymyxin B hemadsorption for treatment of endotoxemic septic shock. Historical data come from a 179-patient subgroup of the previous trial of adult critically ill patients with septic shock, multiple organ failure and an endotoxin activity of 0.60-0.89. The trial intervention consisted of two polymyxin B hemoadsorption treatments (2 h each) completed within 24 h of enrollment.
In our simulations for a new trial of 150 patients, a range of hypothetical results were observed. Across a range of baseline risks and treatment effects and four ways of including historical data, we demonstrate an increase in power with the use of clinically defensible incorporation of historical data. In one possible trial result, for example, with an observed reduction in risk of mortality from 44 to 37%, the probability of benefit is 96% with a fixed weight of 75% on prior data and 90% with a commensurate (adaptive-weighting) prior; the same data give an 80% probability of benefit if historical data are ignored.
Using Bayesian methods and a biologically justifiable use of historical data in a prior distribution yields a study design with higher power than a conventional design that ignores relevant historical data. Bayesian methods may be a viable option for trials in critical care medicine where beneficial treatments have been elusive.
鉴于最近 COVID-19 平台试验的成功，贝叶斯统计方法已成为治疗脓毒症等复杂、异质综合征的一种选择。然而，研究设计需要仔细考虑如何使用贝叶斯方法，在通过先验分布纳入历史数据的不同选择以及最终分析的方式方面，如何改变统计效力。我们当前分析的目的是评估通过先验分布使用不同历史数据以及分析类型如何影响拟议试验的结果，该试验将使用贝叶斯统计方法进行分析。
我们进行了一项模拟研究，纳入了来自美国和加拿大一项多中心、随机临床试验的历史数据，该试验使用聚肌胞素 B 血液吸附治疗内毒素性脓毒性休克。历史数据来自先前试验中 179 例成年危重病患者亚组的数据，这些患者患有脓毒性休克、多器官衰竭和内毒素活性为 0.60-0.89。试验干预包括在入组后 24 小时内完成两次聚肌胞素 B 血液吸附治疗（每次 2 小时）。
在我们对 150 例患者的新试验模拟中，观察到了一系列假设结果。在一系列基线风险和治疗效果以及四种纳入历史数据的方式下，我们证明了通过使用临床合理的方式纳入历史数据，可以提高效力。例如，在一种可能的试验结果中，观察到死亡率从 44%降低到 37%，则固定权重为 75%，基于先验数据的获益概率为 96%，而与之相当的（自适应加权）先验则为 90%；如果忽略历史数据，则获益概率为 80%。
在贝叶斯方法和基于先验分布中使用生物合理的历史数据，可以产生比忽略相关历史数据的传统设计更有力的研究设计。在有益治疗方法难以捉摸的重症监护医学试验中，贝叶斯方法可能是一种可行的选择。
