Department of Data Science, Taiho Pharmaceutical Co., Ltd, Tokyo, Japan.
Department of Management Science, Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan.
Pharm Stat. 2020 Mar;19(2):126-136. doi: 10.1002/pst.1973. Epub 2020 Feb 17.
Progression-free survival is recognized as an important endpoint in oncology clinical trials. In clinical trials aimed at new drug development, the target population often comprises patients that are refractory to standard therapy with a tumor that shows rapid progression. This situation would increase the bias of the hazard ratio calculated for progression-free survival, resulting in decreased power for such patients. Therefore, new measures are needed to prevent decreasing the power in advance when estimating the sample size. Here, I propose a novel calculation procedure to assume the hazard ratio for progression-free survival using the Cox proportional hazards model, which can be applied in sample size calculation. The hazard ratios derived by the proposed procedure were almost identical to those obtained by simulation. The hazard ratio calculated by the proposed procedure is applicable to sample size calculation and coincides with the nominal power. Methods that compensate for the lack of power due to biases in the hazard ratio are also discussed from a practical point of view.
无进展生存期被认为是肿瘤临床试验中的一个重要终点。在旨在开发新药的临床试验中，目标人群通常包括对标准治疗具有耐药性的患者，这些患者的肿瘤具有快速进展的特征。这种情况会增加计算无进展生存期风险比的偏倚，从而降低此类患者的效力。因此，需要新的措施来预防在估计样本量时提前降低效力。在这里，我提出了一种新的计算程序，该程序可以使用 Cox 比例风险模型来假设无进展生存期的风险比，从而可以应用于样本量计算。所提出的程序得出的风险比与通过模拟获得的风险比几乎相同。所提出的程序计算的风险比适用于样本量计算，并与名义功效一致。还从实际角度讨论了补偿由于风险比偏差而导致的功效不足的方法。
