论文标题
评估COVID 19药物的不精确试验的统计决策属性
Statistical Decision Properties of Imprecise Trials Assessing COVID-19 Drugs
论文作者
论文摘要
随着COVID-19的大流行的进展，研究人员报告了比较标准护理与实验药物增加的标准护理的随机试验结果。试验的样本量很小，因此对治疗效果的估计是不精确的。看到不精确，阅读研究文章的临床医生可能会发现很难决定何时治疗实验药物的患者。无论采用哪种决策标准，总是有一些概率将试验结果中的随机变化导致处方优化治疗。比较标准护理和创新时的常规做法是，只有在估计的治疗效果是积极且具有统计学意义的情况下，才能选择创新。该实践将标准护理视为现状。为了评估决策标准，我们使用了近似概念的概念，该概念共同考虑了决策错误的概率和幅度。从这个角度来看，有吸引力的决策标准是经验成功规则，该规则选择了试验中观察到的平均患者预后最高的治疗方法。考虑到最近和正在进行的COVID-19试验的设计，我们表明，经验成功规则比基于假设检验的普遍决策标准产生的治疗结果更接近最佳。
As the COVID-19 pandemic progresses, researchers are reporting findings of randomized trials comparing standard care with care augmented by experimental drugs. The trials have small sample sizes, so estimates of treatment effects are imprecise. Seeing imprecision, clinicians reading research articles may find it difficult to decide when to treat patients with experimental drugs. Whatever decision criterion one uses, there is always some probability that random variation in trial outcomes will lead to prescribing sub-optimal treatments. A conventional practice when comparing standard care and an innovation is to choose the innovation only if the estimated treatment effect is positive and statistically significant. This practice defers to standard care as the status quo. To evaluate decision criteria, we use the concept of near-optimality, which jointly considers the probability and magnitude of decision errors. An appealing decision criterion from this perspective is the empirical success rule, which chooses the treatment with the highest observed average patient outcome in the trial. Considering the design of recent and ongoing COVID-19 trials, we show that the empirical success rule yields treatment results that are much closer to optimal than those generated by prevailing decision criteria based on hypothesis tests.
