In competing risks models, cumulative incidence functions are commonly compared to infer differences between groups. Many existing inference methods, however, struggle when these functions cross during the time frame of interest. To address this problem, we investigate a test statistic based on the area between cumulative incidence functions. As the corresponding limiting distribution depends on quantities that are typically unknown, we propose a wild bootstrap approach to obtain a feasible and asymptotically valid two-sample test. The finite sample performance of the proposed method, in comparison with existing methods, is examined in an extensive simulation study.

翻译：在竞争风险模型中，通常通过比较累积发生率函数来推断组间差异。然而，当这些函数在关注的时间范围内发生交叉时，许多现有推断方法难以处理。为解决该问题，我们研究了一种基于累积发生率函数间面积的检验统计量。由于相应的极限分布依赖于通常未知的参量，我们提出了一种野生自助法来获得可行且渐近有效的双样本检验。通过大量模拟研究，我们考察了所提方法在有限样本下的性能，并与现有方法进行了比较。