Kotalik Ales, Eaton Anne, Lian Qinshu, Serrano Carlos, Connett John, Neaton James D
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Clin Trials. 2019 Dec;16(6):626-634. doi: 10.1177/1740774519868233. Epub 2019 Aug 7.
Composite outcomes, which combine multiple types of clinical events into a single outcome, are common in clinical trials. The usual analysis considers the time to first occurrence of any event in the composite. The major criticisms of such an approach are (1) this implicitly treats the outcomes as if they were of equal importance, but they often vary in terms of clinical relevance and severity, (2) study participants often experience more than one type of event, and (3) often less severe events occur before more severe ones, but the usual analysis disregards any information beyond that first event.
A novel approach, referred to as the win ratio, which addresses the aforementioned criticisms of composite outcomes, is illustrated with a re-analysis of data on fatal and non-fatal cardiovascular disease time-to-event outcomes reported for the Multiple Risk Factor Intervention Trial. In this trial, 12,866 participants were randomized to a special intervention group ( = 6428) or a usual care ( = 6438) group. Non-fatal outcomes were ranked by risk of cardiovascular disease death up to 20 years after trial. In one approach, participants in the special intervention and usual care groups were first matched on coronary heart disease risk at baseline and time of enrollment. Each matched pair was categorized as a winner or loser depending on which one experienced a cardiovascular disease death first. If neither died of cardiovascular disease causes, they were evaluated on the most severe non-fatal outcome. This process continued for all the non-fatal outcomes. A second win ratio statistic, obtained from Cox partial likelihood, was also estimated. This statistic provides a valid estimate of the win ratio using multiple events if the marginal and conditional survivor functions of each outcome satisfy proportional hazards. Loss ratio statistics (inverse of win ratios) are compared to hazard ratios from the usual first event analysis. A larger 11-event composite was also considered.
For the 7-event cardiovascular disease composite, the previously reported first event analysis based on 581 events in the special intervention group and 652 events in the usual care group yielded a hazard ratio (95% confidence interval) of 0.89 (0.79-0.99), compared to 0.86 (0.77-0.97) and 0.91 (0.81-1.02) for the severity ranked estimates. Results for the 11-event composite also confirmed the findings of the first event analysis.
The win ratio analysis was able to leverage information collected past the first experienced event and rank events by severity. The results were similar to and confirmed previously reported traditional first event analysis. The win ratio statistic is a useful adjunct to the traditional first event analysis for trials with composite outcomes.
复合结局将多种类型的临床事件合并为单一结局,在临床试验中很常见。通常的分析考虑复合结局中任何事件首次发生的时间。这种方法的主要批评意见包括:(1)这隐含地将各个结局视为同等重要,但它们在临床相关性和严重程度方面往往存在差异;(2)研究参与者经常经历不止一种类型的事件;(3)通常较轻的事件会在更严重的事件之前发生,但常规分析忽略了首个事件之外的任何信息。
一种称为获胜率的新方法解决了对复合结局的上述批评,通过对多重危险因素干预试验报告的致命和非致命心血管疾病事件发生时间结局数据进行重新分析进行说明。在该试验中,12866名参与者被随机分配到特殊干预组(n = 6428)或常规护理组(n = 6438)。非致命结局根据试验后长达20年的心血管疾病死亡风险进行排序。在一种方法中,特殊干预组和常规护理组的参与者首先在基线和入组时根据冠心病风险进行匹配。根据哪一方首先经历心血管疾病死亡,将每对匹配的参与者分类为胜者或败者。如果双方均未死于心血管疾病原因,则根据最严重的非致命结局对他们进行评估。对所有非致命结局都持续这个过程。还估计了从Cox偏似然性获得的第二个获胜率统计量。如果每个结局的边际和条件生存函数满足比例风险,则该统计量使用多个事件提供获胜率的有效估计。将损失率统计量(获胜率的倒数)与常规首个事件分析的风险比进行比较。还考虑了一个更大的11事件复合结局。
对于7事件心血管疾病复合结局,先前报告的基于特殊干预组581例事件和常规护理组652例事件的首个事件分析得出风险比(95%置信区间)为0.89(0.79 - 0.99),相比之下,按严重程度排序的估计值为0.86(0.77 - 0.97)和0.91(0.81 - 1.02)。11事件复合结局的结果也证实了首个事件分析的结果。
获胜率分析能够利用首个经历事件之后收集的信息,并按严重程度对事件进行排序。结果与先前报告的传统首个事件分析相似并得到了证实。对于有复合结局的试验,获胜率统计量是传统首个事件分析的有用辅助手段。