Does Remdesivir Lower COVID-19 Mortality? A Subgroup Analysis of Hospitalized Adults Receiving Supplemental Oxygen.

IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Statistics in Medicine Pub Date : 2024-12-10 Epub Date: 2024-10-10 DOI:10.1002/sim.10241
Gail E Potter, Michael A Proschan
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Abstract

The first Adaptive COVID-19 Treatment Trial (ACTT-1) showed that remdesivir improved COVID-19 recovery time compared with placebo in hospitalized adults. The secondary outcome of mortality was almost significant overall (p = 0.07) and highly significant for people receiving supplemental oxygen at enrollment (p = 0.002), suggesting a mortality benefit concentrated in this group. We explore analysis methods that are helpful when a single subgroup benefits from treatment and apply them to ACTT-1, using baseline oxygen use to define subgroups. We consider two questions: (1) is the remdesivir effect for people receiving supplemental oxygen real, and (2) does this effect differ from the overall effect? For Question 1, we apply a Bonferroni adjustment to subgroup-specific hypothesis tests and the Westfall and Young permutation test, which is valid when small cell counts preclude normally distributed test statistics (a frequently unexamined condition in subgroup analyses). For Question 2, we introduce Qmax, the largest standardized difference between subgroup-specific effects and the overall effect. Qmax simultaneously tests whether any subgroup effect differs from the overall effect and identifies the subgroup benefitting most. We demonstrate that Qmax strongly controls the familywise error rate (FWER) when test statistics are normally distributed with no mean-variance relationship. We compare Qmax to a related permutation test, SEAMOS, which was previously proposed but not extensively applied or tested. We show that SEAMOS can have inflated Type 1 error under the global null when control arm event rates differ between subgroups. Our results support a mortality benefit from remdesivir in people receiving supplemental oxygen.

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雷米地韦能降低 COVID-19 死亡率吗?对接受辅助供氧的住院成人的分组分析。
首次 COVID-19 适应性治疗试验(ACTT-1)显示,与安慰剂相比,雷米替韦能缩短住院成人的 COVID-19 恢复时间。死亡率这一次要结果在总体上几乎是显著的(p = 0.07),而在入组时接受辅助供氧的患者中则是高度显著的(p = 0.002),这表明死亡率获益主要集中在这一群体中。我们探讨了当单一亚组从治疗中获益时的分析方法,并将其应用于 ACTT-1,使用基线用氧量来定义亚组。我们考虑了两个问题:(1) 雷米替韦对接受辅助供氧者的效果是否真实;(2) 这种效果与总体效果是否存在差异?对于问题 1,我们对亚组特定假设检验以及 Westfall 和 Young permutation 检验进行了 Bonferroni 调整,该检验在细胞数较少而检验统计量不符合正态分布的情况下有效(这是亚组分析中经常出现的一种未审查情况)。对于问题 2,我们引入了 Qmax,即亚组效应与总体效应之间的最大标准化差异。Qmax 可同时检验任何亚组效应是否与总体效应不同,并确定受益最大的亚组。我们证明,当测试统计量呈正态分布且无均值-方差关系时,Qmax 能有效控制族内误差率 (FWER)。我们将 Qmax 与相关的置换检验 SEAMOS 进行了比较。我们发现,当控制臂事件发生率在不同亚组之间存在差异时,SEAMOS 在全局空值下可能会产生夸大的 1 类误差。我们的研究结果表明,雷米替韦对接受氧气补充治疗的患者的死亡率有益。
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来源期刊
Statistics in Medicine
Statistics in Medicine 医学-公共卫生、环境卫生与职业卫生
CiteScore
3.40
自引率
10.00%
发文量
334
审稿时长
2-4 weeks
期刊介绍: The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.
期刊最新文献
A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk. Does Remdesivir Lower COVID-19 Mortality? A Subgroup Analysis of Hospitalized Adults Receiving Supplemental Oxygen. Modeling Chronic Disease Mortality by Methods From Accelerated Life Testing. A Nonparametric Global Win Probability Approach to the Analysis and Sizing of Randomized Controlled Trials With Multiple Endpoints of Different Scales and Missing Data: Beyond O'Brien-Wei-Lachin. Causal Inference for Continuous Multiple Time Point Interventions.
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