背景:净收益是任何类型的终点的效果度量,包括事件发生的时间结果,并能提供直观和有临床意义的解释。它被定义为来自实验臂的随机选择的受试者比来自对照臂的随机选择的受试者存活至少更长的临床相关时间的概率。在肿瘤临床试验中,诸如治疗切换之类的并发事件很常见,这可能会导致信息审查;然而,传统的净收益方法无法处理。在这项研究中,我们提出了一种新的估计器,该估计器使用了审查加权逆概率(IPCW)方法,并说明了一项具有治疗切换的肿瘤学临床试验(SHIVA研究)在该估计和框架下应用所提出的方法.
方法:可以使用每个治疗组的生存功能来估计净收益。所提出的估计器基于通过审查加权方法的逆概率估计的生存函数,该方法可以处理与协变量相关的审查。进行了模拟研究,以评估在几种情况下提出的估计器的操作特性;我们改变了生存曲线的形状,治疗效果,协变量对审查的影响,审查的比例,净收益的阈值,和样本量。我们还将常规方法(Péron或Gehan的评分规则)和提出的方法应用于SHIVA研究。
结果:我们的模拟研究表明,与现有的估计器相比,所提出的估计器在协变量依赖的审查下提供了较少的偏差结果。当将提出的方法应用于SHIVA研究时,我们能够通过合并具有不同估计和策略的协变量的信息来估计净获益,以解决治疗转换的并发事件.然而,在假设策略下,所提出方法的估计值与上述常规方法的估计值相似。
结论:我们提出了一种新的净收益估计器,可以包括协变量,以解释可能的信息审查。我们还提供了使用估计和框架进行治疗切换的肿瘤学临床试验的拟议方法的说明性分析。我们提出的新估计器适用于处理可能导致协变量依赖审查的并发事件。
The net benefit is an effect measure for any type of endpoint, including the time-to-event outcome, and can provide intuitive and clinically meaningful interpretation. It is defined as the probability of a randomly selected subject from the experimental arm surviving by at least a clinically relevant time longer than a randomly selected subject from the control arm. In oncology clinical trials, an intercurrent event such as treatment switching is common, which potentially causes informative censoring; nevertheless, conventional methods for the net benefit are not able to deal with it. In this study, we proposed a new estimator using the inverse probability of censoring weighting (IPCW) method and illustrated an oncology clinical trial with treatment switching (the SHIVA study) to apply the proposed method under the estimand framework.
The net benefit can be estimated using the survival functions of each treatment group. The proposed estimator was based on the survival functions estimated by the inverse probability of the censoring weighting method that can handle covariate-dependent censoring. The simulation study was undertaken to evaluate the operating characteristics of the proposed estimator under several scenarios; we varied the shapes of the survival curves, treatment effect, covariates effect on censoring, proportion of the censoring, threshold of the net benefit, and sample size. We also applied conventional methods (the scoring rules by Péron or Gehan) and the proposed method to the SHIVA study.
Our simulation study showed that the proposed estimator provided less biased results under the covariate-dependent censoring than existing estimators. When applying the proposed method to the SHIVA study, we were able to estimate the net benefit by incorporating the information of the covariates with different estimand strategies to address the intercurrent event of the treatment switching. However, the estimates of the proposed method and those of the aforementioned conventional methods were similar under the hypothetical strategy.
We proposed a new estimator of the net benefit that can include covariates to account for the possibly informative censoring. We also provided an illustrative analysis of the proposed method for the oncology clinical trial with treatment switching using the estimand framework. Our proposed new estimator is suitable for handling the intercurrent events that can potentially cause covariate-dependent censoring.