Brittain E, Palensky J, Blood J, Wittes J
Statistics Collaborative, Inc., Washington, DC 20036, USA.
Stat Med. 1997 Mar 30;16(6):681-93. doi: 10.1002/(sici)1097-0258(19970330)16:6<681::aid-sim487>3.0.co;2-h.
Because many randomized clinical trials study more than one important outcome variable, evaluation of efficacy is often difficult and not completely satisfactory. This paper considers the use of a procedure for endpoint determination described by Follmann et al., that allows raters to integrate subjectively all relevant information about an individual's clinical course into a single univariate assessment. To explore the method's feasibility, we tested the procedure with data from a completed clinical trial, the Systolic Hypertension in the Elderly Program (SHEP). We provided raters blinded to treatment assignment with cards that schematically represent the clinical trajectories of SHEP study participants. The raters independently ranked these trajectories. The method combined ranks across raters to determine a single rank for each study participant; we used a rank procedure to test treatment effect. The major findings were: (i) the raters showed a high level of concordance of rankings; (ii) tests of treatment effect were highly statistically significant; (iii) three statistical methods were effective for implementing the ranking in the large study size case. These methods were use of: (a) scoring rules; (b) incomplete block designs, and (c) categorical ranking.
由于许多随机临床试验研究的重要结局变量不止一个,疗效评估往往困难且不尽人意。本文考虑使用Follmann等人描述的一种确定终点的方法,该方法允许评估者将关于个体临床过程的所有相关信息主观整合到单一的单变量评估中。为了探究该方法的可行性,我们用一项已完成的临床试验——老年收缩期高血压计划(SHEP)的数据对该方法进行了测试。我们向对治疗分配不知情的评估者提供了示意SHEP研究参与者临床轨迹的卡片。评估者对这些轨迹进行独立排序。该方法将评估者的排序合并,为每个研究参与者确定一个单一排序;我们使用排序程序来检验治疗效果。主要发现如下:(i)评估者的排序一致性很高;(ii)治疗效果检验在统计学上具有高度显著性;(iii)三种统计方法在大规模研究中对实施排序有效。这些方法包括:(a)评分规则;(b)不完全区组设计;(c)分类排序。