Yale Cancer Center, Yale University, New Haven, Connecticut.
Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut.
JAMA Oncol. 2018 Apr 12;4(4):e175092. doi: 10.1001/jamaoncol.2017.5092.
Many large adjuvant clinical trials end up underpowered because of fewer than expected events in the control arm. Ensuring a minimum number of events would result in more informative trials.
To calculate individualized residual risk estimates using residual risk prediction software and assess whether defining eligibility based on a minimum residual risk threshold could increase the reliability of clinical trial power calculations compared with eligibility criteria based on tumor size and nodal status.
DESIGN, SETTING, AND PARTICIPANTS: We estimated residual risk in 443 consecutive patients with early-stage breast cancer and assessed clinical trial power as a function of residual risk distribution among the accrued patients. We defined residual risk as the risk of recurrence that remains despite receipt of standard-of-care therapy; this risk is determined by baseline prognostic risk and by the improvement from adjuvant therapy. We performed trial simulations to examine how the power of a 2-arm, 1:1 randomized clinical trial would change as the residual risk distribution of the trial population that met eligibility criteria based on tumor size and nodal status changes. We also simulated trials that use a minimum residual risk value as eligibility criterion.
Residual risk; clinical trial power as a function of residual risk distribution among the patients.
In the 443 patients (mean [SD] age, 56.1 [12.3] years; range, 23-89 years), baseline prognostic and residual risks differed substantially: 328 (74%) patients had more than 20% baseline risk of recurrence; however, after adjustment for treatment effect only 12 (27%) had more than 20% residual risk. We assessed residual risk distribution in patient cohorts that met tumor size- and nodal status-based eligibility criteria for 3 currently accruing randomized adjuvant trials; the median residual risks were 28% (interquartile range [IQR], 25%-31%), 22% (IQR, 15%-28%), and 22% (IQR, 15%-28%), respectively, indicating that the power of these trials could vary unpredictably. Simulations showed that trials that use anatomical risk-based eligibility criteria can become underpowered if they accrue patients with low residual risk despite all participants meeting eligibility requirements. Using a minimum required residual risk threshold as eligibility criterion produced more reliable power calculations.
When tumor size and nodal status are used to determine trial eligibility, the residual risk of recurrence can vary broadly, leading to unstable power estimates. The success of future adjuvant trials could be improved by defining patient eligibility based on a minimal residual risk of recurrence, and these trials can achieve a prespecified power with smaller sample sizes.
许多大型辅助临床试验最终因对照臂中预期事件少于预期而效力不足。确保达到最小事件数可使试验结果更具信息性。
使用残留风险预测软件计算个体化残留风险估计，并评估基于最小残留风险阈值定义合格性是否会比基于肿瘤大小和淋巴结状态的合格性标准更能提高临床试验效能计算的可靠性。
设计、地点和参与者：我们在 443 例早期乳腺癌连续患者中估计了残留风险，并根据累积患者中残留风险的分布评估了临床试验效能。我们将残留风险定义为尽管接受了标准治疗仍存在的复发风险；该风险由基线预后风险和辅助治疗的改善来决定。我们进行了试验模拟，以检查当符合基于肿瘤大小和淋巴结状态的合格性标准的试验人群的残留风险分布发生变化时，2 臂、1:1 随机临床试验的效能如何变化。我们还模拟了使用最小残留风险值作为合格性标准的试验。
残留风险；作为患者残留风险分布的函数的临床试验效能。
在 443 例患者中（平均[标准差]年龄，56.1[12.3]岁；范围，23-89 岁），基线预后和残留风险差异很大：328 例（74%）患者的复发基线风险超过 20%；然而，经治疗效果调整后，仅有 12 例（27%）患者的残留风险超过 20%。我们评估了 3 项正在进行的随机辅助试验中符合肿瘤大小和淋巴结状态的合格性标准的患者队列的残留风险分布；中位数残留风险分别为 28%（四分位距[IQR]，25%-31%）、22%（IQR，15%-28%）和 22%（IQR，15%-28%），这表明这些试验的效能可能不可预测地变化。模拟表明，如果即使所有参与者均符合资格要求，试验仍招募残留风险较低的患者，则基于解剖学风险的合格性标准的试验可能效力不足。使用最小残留风险阈值作为合格性标准可产生更可靠的效能计算。
当使用肿瘤大小和淋巴结状态来确定试验合格性时，复发的残留风险可能会广泛变化，导致不稳定的效能估计。通过基于复发的最小残留风险定义患者合格性，未来辅助试验的成功率可以提高，并且这些试验可以通过较小的样本量实现预定的效能。
