In survival analysis with competing risks, the treatment effect is typically expressed using cause-specific or subdistribution hazard ratios, both relying on proportional hazards assumptions. This paper proposes a nonparametric approach to analyze competing risks data based on generalized pairwise comparisons (GPC). GPC estimate the net benefit, defined as the probability that a patient from the treatment group has a better outcome than a patient from the control group minus the probability of the opposite situation, by comparing all pairs of patients taking one patient from each group. GPC allow using clinically relevant thresholds and simultaneously analyzing multiple prioritized endpoints. We show that under proportional subdistribution hazards, the net benefit for competing risks settings can be expressed as a decreasing function of the subdistribution hazard ratio, taking a value 0 when the latter equals 1. We propose four net benefit estimators dealing differently with censoring. Among them, the Péron estimator uses the Aalen-Johansen estimator of the cumulative incidence functions to classify the pairs for which the patient with the best outcome could not be determined due to censoring. We use simulations to study the bias of these estimators and the size and power of the tests based on the net benefit. The Péron estimator was approximately unbiased when the sample size was large and the censoring distribution's support sufficiently wide. With one endpoint, our approach showed a comparable power to a proportional subdistribution hazards model even under proportional subdistribution hazards. An application of the methodology in oncology is provided.

中文翻译：

---

基于广义成对比较的涉及竞争风险的临床试验中治疗效果的新测量方法

在具有竞争风险的生存分析中，治疗效果通常使用特定原因或子分布风险比来表示，两者都依赖于比例风险假设。本文提出了一种基于广义成对比较 (GPC) 的非参数方法来分析竞争风险数据。GPC 估计净收益，定义为治疗组患者比对照组患者有更好结果的概率减去相反情况的概率，通过比较从每组中抽取一名患者的所有患者对。GPC 允许使用临床相关阈值并同时分析多个优先终点。我们表明，在比例子分布风险下，竞争风险设置的净收益可以表示为子分布风险比的递减函数，当后者等于 1 时取值 0。我们提出了四种不同处理审查的净收益估计量。其中，Péron 估计器使用累积发生率函数的 Aalen-Johansen 估计器对因审查而无法确定具有最佳结果的患者的对进行分类。我们使用模拟来研究这些估计量的偏差以及基于净收益的测试的规模和功效。当样本量很大并且审查分布的支持足够宽时，Péron 估计量近似无偏。一个端点，即使在比例子分布风险下，我们的方法也显示出与比例子分布风险模型相当的功效。提供了该方法在肿瘤学中的应用。