Owkin Inc, New York, USA.
Trials. 2023 Jun 6;24(1):380. doi: 10.1186/s13063-023-07375-0.
Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing the increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the Cancer Genome Atlas (TCGA) cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulations demonstrate that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate (C-index) and with the cumulative incidence of the event in the trial. For a covariate that has an intermediate prognostic performance (C-index=0.65), the reduction of sample size varies from 3.1% when cumulative incidence is of 10% to 29.1% when the cumulative incidence is of 90%. Broadening eligibility criteria usually reduces statistical power while our simulations show that it can be maintained with adequate covariate adjustment. In a simulation of adjuvant trials in HCC, we find that the number of patients screened for eligibility can be divided by 2.4 when broadening eligibility criteria. Last, we find that the Cox-Snell [Formula: see text] is a conservative estimation of the reduction in sample size requirements provided by covariate adjustment. Overall, more systematic adjustment for prognostic covariates leads to more efficient and inclusive clinical trials especially when cumulative incidence is large as in metastatic and advanced cancers. Code and results are available at https://github.com/owkin/CovadjustSim .
调整预后协变量可提高随机试验的统计学效能。对于连续结局的试验，影响效能增加的因素是众所周知的。在这里，我们研究了哪些因素会影响事件时间试验的效能和样本量需求。我们同时考虑了参数模拟和来自癌症基因组图谱（TCGA）肝细胞癌（HCC）患者队列的模拟，以评估协变量调整如何降低样本量需求。模拟表明，协变量调整的益处随着调整协变量的预后性能（C 指数）和试验中事件的累积发生率而增加。对于具有中等预后性能的协变量（C 指数=0.65），当累积发生率为 10%时，样本量减少 3.1%，而当累积发生率为 90%时，样本量减少 29.1%。放宽入选标准通常会降低统计学效能，但我们的模拟表明，通过适当的协变量调整可以维持效能。在 HCC 辅助试验的模拟中，我们发现当放宽入选标准时，可将符合入选条件的患者筛查数量减少 2.4 倍。最后，我们发现 Cox-Snell [Formula: see text]是协变量调整提供的样本量需求减少的保守估计。总体而言，更系统地调整预后协变量可使临床试验更有效和更具包容性，特别是在累积发生率较大（如转移性和晚期癌症）的情况下。代码和结果可在 https://github.com/owkin/CovadjustSim 上获得。
