Bristol-Myers Squibb (BMS), 300 Connell Drive, Berkeley Heights, NJ, 07922, United States.
New London, CT, 06320, United States.
Crit Rev Oncol Hematol. 2021 Jun;162:103350. doi: 10.1016/j.critrevonc.2021.103350. Epub 2021 May 12.
In trials of novel immuno-oncology drugs, the proportional hazards (PH) assumption often does not hold for the primary time-to-event (TTE) efficacy endpoint, likely due to the unique mechanism of action of these drugs. In practice, when it is anticipated that PH may not hold for the TTE endpoint with respect to treatment, the sample size is often still calculated under the PH assumption, and the hazard ratio (HR) from the Cox model is still reported as the primary measure of the treatment effect. Sensitivity analyses of the TTE data using methods that are suitable under non-proportional hazards (non-PH) are commonly pre-planned. In cases where a substantial deviation from the PH assumption is likely, we suggest designing the trial, calculating the sample size and analyzing the data, using a suitable method that accounts for non-PH, after gaining alignment with regulatory authorities. In this comprehensive review article, we describe methods to design a randomized oncology trial, calculate the sample size, analyze the trial data and obtain summary measures of the treatment effect in the presence of non-PH. For each method, we provide examples of its use from the recent oncology trials literature. We also summarize in the Appendix some methods to conduct sensitivity analyses for overall survival (OS) when patients in a randomized trial switch or cross-over to the other treatment arm after disease progression on the initial treatment arm, and obtain an adjusted or weighted HR for OS in the presence of cross-over. This is an example of the treatment itself changing at a specific point in time - this cross-over may lead to a non-PH pattern of diminishing treatment effect.
在新型免疫肿瘤药物的临床试验中，主要时间事件（TTE）疗效终点的比例风险（PH）假设通常不成立，这可能是由于这些药物的独特作用机制。在实践中，当预计 TTE 终点的 PH 假设不成立时，通常仍根据 PH 假设计算样本量，并报告 Cox 模型的风险比（HR）作为治疗效果的主要衡量标准。通常会预先计划对 TTE 数据进行适合非比例风险（非 PH）的敏感性分析。在 PH 假设可能存在重大偏差的情况下，我们建议在获得监管机构的认可后，使用适合非 PH 的合适方法设计试验、计算样本量和分析数据。在这篇综合综述文章中，我们描述了在存在非 PH 的情况下，设计随机肿瘤学试验、计算样本量、分析试验数据和获得治疗效果汇总度量的方法。对于每种方法，我们都从最近的肿瘤学试验文献中提供了其使用示例。我们还在附录中总结了在随机试验中，当患者在初始治疗臂疾病进展后切换或交叉到另一个治疗臂时，对总体生存（OS）进行敏感性分析的一些方法，并在交叉时获得 OS 的调整或加权 HR。这是治疗本身在特定时间点发生变化的一个例子——这种交叉可能导致治疗效果的 PH 模式逐渐减弱。
