A simple, assumption-free, and clinically interpretable approach for analysis of modified Rankin outcomes.
A simple, assumption-free, and clinically interpretable approach for analysis of modified Rankin outcomes.
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DOI：
10.1161/strokeaha.111.632935
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发表时间：
2012-03
期刊：
影响因子：
8.3
通讯作者：
Hess DC
中科院分区：
文献类型：
--
作者：
Howard G;Waller JL;Voeks JH;Howard VJ;Jauch EC;Lees KR;Nichols FT;Rahlfs VW;Hess DC
关于分析改良Rankin评分(MRS)的方法还存在争议，MRS是急性卒中试验中使用的最常见的功能结果量表。我们建议使用测试来评估治疗差异，以解决以下指标：“如果从每个治疗组中随机选择一个患者，如果他们的结果不同，接受研究治疗的患者比接受标准治疗的患者有多大机会获得更好的结果？”这种方法具有患者和临床医生容易理解的治疗效果的相关声明，并导致通过与Mann-Whitney U检验密切相关的测试对治疗差异进行统计测试(又名。Wilcoxon Rank-Sum检验)，可以通过排列检验(也称为随机化测试)。我们表明，置换测试与其他评估MRS量表顺序结果的方法一样强大，并提供了几个对比替代方法的例子的数据。虽然许多分析MRS结果的方法具有基本相似的统计性能，但该方法：1)从序数尺度捕获信息，2)提供患者和临床医生都能理解的强大的临床解释，3)至少具有与其他序数方法相同的能力，4)避免在参数化中的假设，5)基于与p值计算相同的基础提供可解释的参数。
There is debate regarding the approach for analysis of modified Rankin scores (mRS), the most common functional outcome scale used in acute stroke trials. We propose to use tests to assess treatment differences addressing the metric “If a patient is chosen at random from each treatment group and if they have different outcomes, what is the chance the patient who got the investigational treatment will have a better outcome than the patient receiving the standard treatment?” This approach has an associated statement of treatment efficacy easily understood by patients and clinicians, and leads to statistical testing of treatment differences by tests closely related to the Mann-Whitney U test (a.k.a. Wilcoxon Rank-Sum test) which can be tested precisely by permutation tests (a.k.a. randomization tests). We show that a permutation test is as powerful as other approaches assessing ordinal outcomes of the mRS scale, and provide data from several examples contrasting alternative approaches. While many approaches to analysis of mRS outcomes have generally similar statistical performances, this proposed approach: 1) captures information from the ordinal scale, 2) provides a powerful clinical interpretation understood by both patients and clinicians, 3) has power at least equivalent to the other ordinal approaches, 4) avoids assumptions in the parameterization, and 5) provides an interpretable parameter based on the same foundation as the calculation of the p-value.