Insitro, South San Francisco, CA, USA.
Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, Boston, MA, USA.
JNCI Cancer Spectr. 2022 Jan 5;6(1). doi: 10.1093/jncics/pkac007.
When designing a comparative oncology trial for an overall or progression-free survival endpoint, investigators often quantify the treatment effect using a difference in median survival times. However, rather than directly designing the study to estimate this difference, it is almost always converted to a hazard ratio (HR) to determine the study size. At the analysis stage, the hazard ratio is utilized for formal analysis, yet because it may be difficult to interpret clinically, especially when the proportional hazards assumption is not met, the observed medians are also reported descriptively. The hazard ratio and median difference contrast different aspects of the survival curves. Whereas the hazard ratio places greater emphasis on late-occurring separation, the median difference focuses locally on the centers of the distributions and cannot capture either short- or long-term differences. Having 2 sets of summaries (a hazard ratio and the medians) may lead to incoherent conclusions regarding the treatment effect. For instance, the hazard ratio may suggest a treatment difference whereas the medians do not, or vice versa. In this commentary, we illustrate these commonly encountered issues using examples from recent oncology trials. We present a coherent alternative strategy that, unlike relying on the hazard ratio, does not require modeling assumptions and always results in clinically interpretable summaries of the treatment effect.
当为总体或无进展生存期终点设计比较肿瘤学试验时,研究人员通常使用中位生存时间差异来量化治疗效果。然而,与其直接设计研究来估计这种差异,通常将其转换为风险比(HR)来确定研究规模。在分析阶段,风险比用于正式分析,但由于其在临床上可能难以解释,尤其是当不符合比例风险假设时,还会描述性地报告观察到的中位数。风险比和中位数差异对比了生存曲线的不同方面。虽然风险比更侧重于晚期发生的分离,但中位数差异则更侧重于分布的中心,无法捕捉短期或长期差异。有 2 组摘要(风险比和中位数)可能会导致关于治疗效果的不一致结论。例如,风险比可能提示治疗差异,而中位数则没有,或者反之亦然。在这篇评论中,我们使用最近肿瘤学试验的例子来说明这些常见问题。我们提出了一种连贯的替代策略,与依赖风险比不同,该策略不需要建模假设,并且始终产生治疗效果的临床可解释摘要。