Lu Ying, Zhao Qian, Zou Jiying, Yan Shiyan, Tamaresis John S, Nelson Lorene, Tu Xin M, Chen Jie, Tian Lu
Department of Biomedical Data Science, Stanford University School of Medicine.
Department of Epidemiology and Population Health, Stanford University School of Medicine.
Stat Biopharm Res. 2022;14(4):408-422. doi: 10.1080/19466315.2022.2085783. Epub 2022 Jul 19.
Complex disorders usually affect multiple symptom domains measured by several outcomes. The importance of these outcomes is often different among patients. Current approaches integrate multiple outcomes without considering patient preferences at the individual level. In this paper, we propose a new composite Desirability of Outcome Ranking (DOOR) that integrates individual level ranking of outcome importance and define a winning probability measuring the overall treatment effect. Stratified randomization can be performed based on the participants' baseline outcome rankings. A Wilcoxon-Mann-Whitney U-statistic is used to average the pairwise DOOR between one treated and one control patient, considering the difference in these patients' ranking of outcome importance. We use both theoretical and empirical methods to examine the statistical properties of our method and to compare with conventional approaches. We conclude that the proposed composite DOOR properly reflects patient-level preferences and can be used in pivotal trials or comparative effectiveness trials for a patient-centered evaluation of overall treatment benefits.
复杂疾病通常会影响由多个结局指标所衡量的多个症状领域。这些结局指标的重要性在患者之间往往存在差异。当前的方法在整合多个结局指标时未考虑个体层面的患者偏好。在本文中，我们提出了一种新的综合结局排名合意性（DOOR）方法，该方法整合了结局重要性的个体层面排名，并定义了一个衡量总体治疗效果的获胜概率。可以根据参与者的基线结局排名进行分层随机化。使用Wilcoxon-Mann-Whitney U统计量来对一名治疗患者和一名对照患者之间的成对DOOR进行平均，同时考虑这些患者在结局重要性排名上的差异。我们使用理论和实证方法来检验我们方法的统计特性，并与传统方法进行比较。我们得出结论，所提出的综合DOOR方法能够恰当地反映患者层面的偏好，可用于关键试验或比较有效性试验，以进行以患者为中心的总体治疗益处评估。
