Division of Stroke Medicine, University of Nottingham, Hospital campus, Nottingham NG5 1PB, UK.
Int J Stroke. 2011 Dec;6(6):472-9. doi: 10.1111/j.1747-4949.2011.00614.x. Epub 2011 Jun 6.
Number-needed-to-treat describes the magnitude of the effect of an intervention, underpins health economic analyses, and is typically calculated for binary events. Ordered categorical outcomes provide more clinical information and their analysis using ordinal approaches is usually more efficient statistically. However, to date, techniques to calculate number-needed-to-treat based on ordinal outcomes for parallel group trials have had important limitations. Aims Numbers-needed-to-treat may be calculated for ordinal data from parallel group trials by using an unmatched comparison of all subjects or by generating matched pairs of patients nested within the study.
The above approaches were assessed and compared with numbers-needed-to-treat calculated for binary outcomes using individual patient data from acute and prevention stroke trials testing the effect of interventions of varying utility and efficacy.
Numbers-needed-to-treat were generally lower numerically for ordinal vs. binary, and matched vs. unmatched analyses, and the lowest in highly efficacious interventions: hemicraniectomy, ordinal matched 2.4 vs. ordinal unmatched 2.5 vs. binary matched 12 vs. binary unmatched 9 (one trial, 12 month outcome); alteplase, 4.5 vs. 6.6 vs. 8.4 vs. 8.4 (one trial with two parts, three-months); aspirin, 42 vs. 58 vs. 76 vs. 80 (one trial, six-months); and stroke units, 3.6-5.3 vs. 6.2 vs. 4.7-5.9 vs. 6.3-7.0 (two trials, three- to 60 months). Similar trends were seen for aspirin/dipyridamole vs. aspirin in secondary prevention, 22 vs. 20 vs. 31 vs. 31 (one trial, 24 months).
Number-needed-to-treat may be calculated for ordinal outcome data derived from parallel group stroke trials; such numbers-needed-to-treat are lower than those calculated for binary outcomes. Their use complements the use of ordinal statistical approaches in the analysis of ordered categorical data.
需要治疗的人数描述了干预措施的效果大小，是健康经济学分析的基础，通常针对二项事件进行计算。有序分类结局提供了更多的临床信息，使用有序方法进行分析在统计学上通常更有效。然而，到目前为止，基于平行组试验的有序分类结局计算需要治疗的人数的技术存在重要的局限性。目的 可以通过使用所有受试者的不匹配比较或生成嵌套在研究中的患者匹配对，为平行组试验的有序数据计算需要治疗的人数。
方法 评估了上述方法，并使用来自急性和预防中风试验的个体患者数据，比较了不同效用和疗效的干预措施的二项结局计算的需要治疗的人数。
结果 与二项结局相比，有序结局和匹配分析的需要治疗的人数通常数值较低，在高效干预措施中最低：半颅骨切除术，有序匹配 2.4 与有序不匹配 2.5 与二项匹配 12 与二项不匹配 9（一项试验，12 个月结局）；阿替普酶，4.5 与 6.6 与 8.4 与 8.4（一项试验，两部分，3 个月）；阿司匹林，42 与 58 与 76 与 80（一项试验，6 个月）；和卒中单元，3.6-5.3 与 6.2 与 4.7-5.9 与 6.3-7.0（两项试验，3 至 60 个月）。阿司匹林/双嘧达莫与阿司匹林在二级预防中的情况类似，22 与 20 与 31 与 31（一项试验，24 个月）。
结论 可以为源自平行组中风试验的有序分类结局数据计算需要治疗的人数；此类需要治疗的人数低于为二项结局计算的人数。它们的使用补充了有序分类数据的有序统计方法分析。
