How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19
How to Quantify and Interpret Treatment Effects in Comparative Clinical Studies of COVID-19
复制标题
DOI：
10.7326/m20-4044
复制
发表时间：
2020-10-20
影响因子：
39.2
通讯作者：
Wei, Lee-Jen
中科院分区：
文献类型：
--
作者：
McCaw, Zachary R.;Tian, Lu;Wei, Lee-Jen
2019冠状病毒病（COVID-19）治疗的临床试验引起了公众的强烈关注。现在比以往任何时候都更需要有效、透明和直观的治疗效果总结，包括疗效和危害。在最近发表和正在进行的评估COVID-19治疗的随机比较试验中，达到积极结果（如恢复或改善）的时间被反复用作主要或关键次要终点。由于患者可能在恢复或改善之前死亡，因此该终点的数据分析面临竞争风险问题。常用的生存分析技术，如Kaplan-Meier方法，通常不适用于这种情况。此外，几乎所有试验都使用风险比量化了治疗效果，这很难解释为积极事件，特别是在存在竞争风险的情况下。以最近两项评估COVID-19治疗（瑞德西韦和恢复期血浆）的试验为例，提出了一种有效、完善但未充分使用的程序，用于估计整个研究期间的累积恢复或改善率曲线。此外，还提出了基于该曲线的直观和临床可解释的治疗疗效总结。鼓励临床研究者考虑在未来的COVID-19研究中应用这些方法来量化治疗效果。
Clinical trials of treatments for coronavirus disease 2019 (COVID-19) draw intense public attention. More than ever, valid, transparent, and intuitive summaries of the treatment effects, including efficacy and harm, are needed. In recently published and ongoing randomized comparative trials evaluating treatments for COVID-19, time to a positive outcome, such as recovery or improvement, has repeatedly been used as either the primary or key secondary end point. Because patients may die before recovery or improvement, data analysis of this end point faces a competing risk problem. Commonly used survival analysis techniques, such as the Kaplan-Meier method, often are not appropriate for such situations. Moreover, almost all trials have quantified treatment effects by using the hazard ratio, which is difficult to interpret for a positive event, especially in the presence of competing risks. Using 2 recent trials evaluating treatments (remdesivir and convalescent plasma) for COVID-19 as examples, a valid, well-established yet underused procedure is presented for estimating the cumulative recovery or improvement rate curve across the study period. Furthermore, an intuitive and clinically interpretable summary of treatment efficacy based on this curve is also proposed. Clinical investigators are encouraged to consider applying these methods for quantifying treatment effects in future studies of COVID-19.