Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study.
Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study.
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在随机对照试验中检测显着的抗病毒药物对COVID-19具有合理样本量的效应：一项建模研究。
DOI：
10.1371/journal.pmed.1003660
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发表时间：
2021-07
期刊：
影响因子：
15.8
通讯作者：
Wakita T
中科院分区：
文献类型：
--
作者：
Iwanami S;Ejima K;Kim KS;Noshita K;Fujita Y;Miyazaki T;Kohno S;Miyazaki Y;Morimoto S;Nakaoka S;Koizumi Y;Asai Y;Aihara K;Watashi K;Thompson RN;Shibuya K;Fujiu K;Perelson AS;Iwami S;Wakita T
开发针对2019冠状病毒病（COVID-19）的有效抗病毒药物是全球卫生优先事项。虽然已经通过体外和体内模型确定了几种候选药物，但来自临床研究的一致和令人信服的证据有限。临床试验证据的缺乏可能部分源于试验设计的不完善。我们研究了如何设计抗病毒药物的临床试验，特别是在随机对照试验中的样本量。进行了建模研究，以帮助理解不一致的临床试验结果背后的原因，并设计更好的临床试验。我们首先分析了纵向病毒载量数据严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）没有抗病毒治疗的宿主内病毒动力学模型。通过聚类方法将拟合的病毒载量分为3个不同的组。估计参数的比较表明，3个不同的组的特征在于不同的病毒衰减率（p值< 0.001）。3组的平均衰变率分别为1.17 d−1（95% CI：1.06至1.27 d−1）、0.777 d−1（0.716至0.838 d−1）和0.450 d−1（0.378至0.522 d−1）。如果病毒动力学的这种异质性与同情使用项目中的治疗分配相关（即，观察研究）。随后，我们通过模拟来模拟抗病毒药物的随机对照试验。将导致病毒复制减少95%至99%的抗病毒作用添加到模型中。为了现实起见，我们假设随机化和治疗在症状发作后有一段时间延迟。使用病毒脱落持续时间作为结局，如果所有患者均入组，无论随机化时间如何，则检测治疗组和安慰剂组（1：1分配）之间统计学显著平均差异的样本量为每组13，603和11，670（当抗病毒效果分别为95%和99%时）。如果仅入组症状发作1天内接受治疗的患者，则样本量减少至584和458（抗病毒效果分别为95%和99%）。我们证实，当使用对数尺度的累积病毒载量作为结果时，样本量也同样减少。我们使用了传统的病毒动力学模型，该模型可能无法完全反映SARS-CoV-2病毒动力学的详细机制。模型需要在参数设置和模型结构方面进行校准，这将产生更可靠的样本量计算。在这项研究中，我们发现，由于感染个体之间病毒动力学的巨大异质性，观察性研究中估计的相关性可能存在偏倚，并且由于样本量小，随机对照试验中的统计学显著性效应可能难以检测。通过在出现症状后立即招募患者，可以大大减少样本量。我们相信这是第一个研究使用病毒动力学模型研究抗病毒治疗临床试验的研究设计。Shingo Iwami及其同事使用病毒动力学模型，研究了在随机对照试验中检测抗病毒药物对COVID-19的显著影响所需的样本量。针对严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）的抗病毒药物的临床研究大多未能观察到统计学上显著的效果，这可能是由于临床试验设计不佳。如何设计抗病毒药物的临床试验还没有得到很好的研究。特别是，需要建立样本量计算方法。SARS-CoV-2病毒动态通过拟合病毒动态模型来定量纵向病毒载量数据。拟合病毒载量的聚类分析显示3个不同的组，其特征在于不同的病毒衰减率，这可能是观察性研究中的混杂因素。模拟随机对照试验表明，如果不考虑治疗开始的时间，样本量将不合理地大（每组> 11，000）。通过仅纳入症状发作后早期入组的患者，样本量显著减少。抗病毒药物的随机对照试验应在症状出现后尽早招募患者，或根据症状出现后的时间设定入选标准，以观察有统计学意义的结果。反映SARS-CoV-2感染特征的更精确模型可能提供更可靠的样本量估计。
Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d−1 (95% CI: 1.06 to 1.27 d−1), 0.777 d−1 (0.716 to 0.838 d−1), and 0.450 d−1 (0.378 to 0.522 d−1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model. Using a viral dynamics model, Shingo Iwami and colleagues investigate the sample sizes required to detect significant antiviral drug effects on COVID-19 in randomized controlled trials. Most clinical studies of antiviral drugs for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have failed to observe a statistically significant effect, which may be due to poor designs of clinical trials. It is not well studied how clinical trials for antiviral drugs should be designed. Especially, sample size calculation methodology needs to be established. SARS-CoV-2 virus dynamics was quantified by fitting a virus dynamic model to longitudinal viral load data. Cluster analysis of the fitted viral loads revealed 3 distinct groups characterized by different virus decay rates, which could be a confounding factor in observational studies. Simulation mimicking randomized controlled trials demonstrated that sample size would be unreasonably large (>11,000 per group) if the timing of treatment initiation is not considered. The sample size is significantly reduced by including only patients enrolled early after symptom onset. Randomized controlled trials for antiviral drugs should recruit patients as early as possible after symptom onset or set inclusion criteria based on the time since symptom onset to observe statistically significant results. More precise models reflecting the features of SARS-CoV-2 infection may provide more reliable sample size estimates.
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