Girbes Armand R J, de Grooth Harm-Jan
Department of Intensive Care Medicine, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands.
Department of Anesthesiology, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands.
J Thorac Dis. 2020 Feb;12(Suppl 1):S101-S109. doi: 10.21037/jtd.2019.10.36.
In this paper we discuss the limitations of large randomized controlled trials with mortality endpoints in patients with critical illness associated diagnoses such as sepsis. When patients with the same syndrome diagnosis do not share the pathways that lead to death (the attributable risk), any therapy can only lead to small effects in these populations. Using Monte Carlo simulations, we show how the syndrome-attributable risks of critical illness-associated diagnoses are likely overestimated using common statistical methods. This overestimation of syndrome-attributable risks leads to a corresponding overestimation of attainable treatment effects and an underestimation of required sample sizes. We demonstrate that larger and more 'pragmatic' randomized trials are not the solution because they decrease therapeutic and diagnostic precision, the therapeutic effect size and the probability of finding a beneficial effect. Finally, we argue that the most logical solution is a renewed focus on mechanistic research into the complexities of critical illness syndromes.
在本文中,我们讨论了针对患有脓毒症等危重症相关诊断的患者进行的、以死亡率为终点的大型随机对照试验的局限性。当患有相同综合征诊断的患者并未共享导致死亡的途径(归因风险)时,任何治疗在这些人群中只能产生微小的效果。通过蒙特卡洛模拟,我们展示了使用常见统计方法时,危重症相关诊断的综合征归因风险可能如何被高估。这种对综合征归因风险的高估会相应地导致对可实现的治疗效果的高估以及对所需样本量的低估。我们证明,规模更大且更“实用”的随机试验并非解决之道,因为它们会降低治疗和诊断的精准度、治疗效果大小以及发现有益效果的概率。最后,我们认为最合乎逻辑的解决办法是重新聚焦于对危重症综合征复杂性的机制研究。