When comparing multiple groups in clinical trials, we are not only interested in whether there is a difference between any groups but rather the location. Such research questions lead to testing multiple individual hypotheses. To control the familywise error rate (FWER), we must apply some corrections or introduce tests that control the FWER by design. In the case of time-to-event data, a Bonferroni-corrected log-rank test is commonly used. This approach has two significant drawbacks: (i) it loses power when the proportional hazards assumption is violated [1] and (ii) the correction generally leads to a lower power, especially when the test statistics are not independent [2]. We propose two new tests based on combined weighted log-rank tests. One as a simple multiple contrast test of weighted log-rank tests and one as an extension of the so-called CASANOVA test [3]. The latter was introduced for factorial designs. We propose a new multiple contrast test based on the CASANOVA approach. Our test promises to be more powerful under crossing hazards and eliminates the need for additional p-value correction. We assess the performance of our tests through extensive Monte Carlo simulation studies covering both proportional and non-proportional hazard scenarios. Finally, we apply the new and reference methods to a real-world data example. The new approaches control the FWER and show reasonable power in all scenarios. They outperform the adjusted approaches in some non-proportional settings in terms of power.

翻译：在临床试验中进行多组比较时，我们不仅关注组间是否存在差异，更关注差异的具体位置。此类研究问题引出了多重独立假设检验的需求。为控制族系错误率（FWER），必须采用校正方法或引入从设计层面控制FWER的检验方法。针对时间-事件数据，通常采用Bonferroni校正的对数秩检验。该方法存在两个显著缺陷：（i）当比例风险假设被违反时检验效能会降低[1]；（ii）校正通常导致检验效能下降，尤其在检验统计量不独立时更为明显[2]。我们提出了两种基于加权对数秩检验组合的新检验方法：一种是简单的加权对数秩检验多重对比检验，另一种是所谓CASANOVA检验[3]的扩展（该检验原为因子设计而提出）。我们基于CASANOVA方法提出了一种新的多重对比检验。新检验有望在交叉风险场景下获得更高检验效能，且无需额外的p值校正。我们通过涵盖比例风险与非比例风险场景的大规模蒙特卡洛模拟研究评估了新检验方法的性能。最后将新方法与参考方法应用于实际数据案例。新方法能有效控制FWER，在所有场景中均表现出合理的检验效能，并在某些非比例风险场景的检验效能方面优于校正方法。