Endpoints for randomized controlled clinical trials for COVID-19 treatments.
Endpoints for randomized controlled clinical trials for COVID-19 treatments.
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DOI：
10.1177/1740774520939938
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发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Jaki T
中科院分区：
文献类型：
--
作者：
Dodd LE;Follmann D;Wang J;Koenig F;Korn LL;Schoergenhofer C;Proschan M;Hunsberger S;Bonnett T;Makowski M;Belhadi D;Wang Y;Cao B;Mentre F;Jaki T
新型冠状病毒引起的疾病（COVID-19）治疗的随机对照试验的终点选择是复杂的。在相当大的不确定性和有限的信息中，必须迅速开始试验，以确定可用作疫情应对措施的一部分的治疗方法。COVID-19的表现是异质的，从几天内好转的轻度疾病到可能持续数周至一个多月并可能导致死亡的重症疾病。虽然死亡率的改善将为治疗的临床意义提供不容置疑的证据，但评估死亡率的研究的样本量很大，可能不切实际，特别是考虑到要评估的多种假定疗法。此外，介于“治愈”和“死亡”之间的患者状态代表了有意义的区别。临床严重程度评分已被提议作为替代方案。然而，严重程度评分的适当概括衡量一直是争论的主题，特别是考虑到COVID-19的可变时间进程。在固定时间点测量的结局，例如第14天治疗组和对照组之间的严重程度评分比较，可能会有错过临床获益时间的风险。一个终点，如改善（或恢复）的时间，避免了时间问题。然而，有些人认为，将序数标度减少到“恢复”与“未恢复”的二元状态将导致功率损失。我们使用模拟模型和最近两项COVID-19治疗试验的数据，评估COVID-19治疗试验可能试验终点的统计功效。固定时间点方法的功效在很大程度上取决于选择用于评价的时间。即使与在最佳时间评价的固定时间点方法相比，事件发生时间方法也具有合理的统计功效。除非事先知道评估治疗效果的最佳时间，否则事件发生时间分析方法在COVID-19背景下具有优势。即使已知最佳时间，事件发生时间方法也可能增加中期分析的把握度。
Endpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between “cure” and “death” represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An end-point such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of “recovered” versus “not recovered.” We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. Power for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time. Time-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.
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影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
158.5
作者：
Cao, B.;Wang, Y.;Wang, C.
通讯作者：
Wang, C.
影响因子：
1.5
作者：
Peterson, Ross L.;Vock, David M.;Neaton, James D.
通讯作者：
Neaton, James D.
影响因子：
8.3
作者：
Saver, Jeffrey L.
通讯作者：
Saver, Jeffrey L.
DOI：
10.1001/jama.2020.6775
发表时间：
2020-01-01
期刊：
JAMA, Journal of the American Medical Association
影响因子：
--
作者：
Richardson, Safiya;Hirsch, Jamie S.;,
通讯作者：
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