Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
National Bureau of Economic Research, Cambridge, Massachusetts, USA.
Health Econ. 2021 May;30(5):1050-1069. doi: 10.1002/hec.4233. Epub 2021 Mar 5.
Comparing median outcomes to gauge treatment effectiveness is widespread practice in clinical and other investigations. While common, such difference-in-median characterizations of effectiveness are but one way to summarize how outcome distributions compare. This paper explores properties of median treatment effects (TEs) as indicators of treatment effectiveness. The paper's main focus is on decisionmaking based on median TEs and it proceeds by considering two paths a decisionmaker might follow. Along one, decisions are based on point-identified differences in medians alongside partially identified median differences; along the other decisions are based on point-identified differences in medians in conjunction with other point-identified parameters. On both paths familiar difference-in-median measures play some role yet in both the traditional standards are augmented with information that will often be relevant in assessing treatments' effectiveness. Implementing either approach is straightforward. In addition to its analytical results the paper considers several policy contexts in which such considerations arise. While the paper is framed by recently reported findings on treatments for COVID-19 and uses several such studies to explore empirically some properties of median-treatment-effect measures of effectiveness, its results should be broadly applicable.
将中位数结果进行比较以衡量治疗效果在临床和其他研究中是一种广泛应用的做法。虽然这种基于中位数的有效性差异描述很常见，但它只是比较结果分布的一种方法。本文探讨了中位数治疗效果（TE）作为治疗效果指标的性质。本文的主要重点是基于中位数 TE 的决策制定，并通过考虑决策者可能遵循的两条路径来进行。其中一条路径是基于中位数的点识别差异以及部分识别的中位数差异做出决策；另一条路径是基于中位数的点识别差异以及其他点识别参数做出决策。在这两条路径上，熟悉的中位数差异衡量标准都发挥了一定作用，但在这两条路径上，传统标准都通过评估治疗效果时通常相关的信息进行了扩展。实施这两种方法都很简单。除了分析结果外，本文还考虑了在几种政策环境中出现的这些考虑因素。虽然本文的框架是基于最近关于 COVID-19 治疗的报告结果，并使用了几项此类研究来实证研究中位数治疗效果衡量有效性的一些性质，但它的结果应该具有广泛的适用性。
