Department of Economics and Institute for Policy Research, Northwestern University, Evanston, IL, USA.
Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland.
Value Health. 2021 May;24(5):641-647. doi: 10.1016/j.jval.2020.11.019. Epub 2021 Mar 9.
Researchers studying treatment of coronavirus disease 2019 (COVID-19) have reported findings of randomized trials comparing standard care with care augmented by experimental drugs. Many trials have small sample sizes, so estimates of treatment effects are imprecise. Hence, clinicians may find it difficult to decide when to treat patients with experimental drugs. 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. We study treatment choice from the perspective of statistical decision theory, which considers treatment options symmetrically when assessing trial findings.
We use the concept of near-optimality to evaluate criteria for treatment choice. This concept jointly considers the probability and magnitude of decision errors. An appealing 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 some COVID-19 trials, we show that the empirical success rule yields treatment choices that are much closer to optimal than those generated by prevailing decision criteria based on hypothesis tests.
Using trial findings to make near-optimal treatment choices rather than perform hypothesis tests should improve clinical decision making.
研究 2019 年冠状病毒病（COVID-19）治疗方法的研究人员报告了比较标准护理与实验药物增强护理的随机试验结果。许多试验的样本量较小，因此治疗效果的估计不准确。因此，临床医生可能难以决定何时用实验药物治疗患者。当比较标准护理和创新时，传统的做法是仅在估计的治疗效果为正且具有统计学意义时选择创新。这种做法将标准护理作为现状。我们从统计决策理论的角度研究治疗选择，该理论在评估试验结果时对称地考虑治疗选择。
我们使用近优性的概念来评估治疗选择的标准。这个概念综合考虑了决策错误的概率和幅度。从这个角度来看，一个吸引人的标准是经验成功规则，它选择试验中观察到的平均患者结局最高的治疗方法。
考虑到一些 COVID-19 试验的设计，我们表明，经验成功规则产生的治疗选择比基于假设检验的流行决策标准生成的治疗选择更接近最优。
使用试验结果做出近优的治疗选择，而不是进行假设检验，应该会改善临床决策。
