Randomized controlled trials (RCTs) in oncology often allow control group participants to crossover to experimental treatments, a practice that, while often ethically necessary, complicates the accurate estimation of long-term treatment effects. When crossover rates are high or sample sizes are limited, commonly used methods for crossover adjustment (such as the rank-preserving structural failure time model, inverse probability of censoring weights, and two-stage estimation (TSE)) may produce imprecise estimates. Real-world data (RWD) can be used to develop an external control arm for the RCT, although this approach ignores evidence from trial subjects who did not crossover and ignores evidence from the data obtained prior to crossover for those subjects who did. This paper introduces ''augmented two-stage estimation'' (ATSE), a method that combines data from non-switching participants in a RCT with an external dataset, forming a ''hybrid non-switching arm''. With a simulation study, we evaluate the ATSE method's performance compared to TSE crossover adjustment and an external control arm approach. Results indicate that performance is dependent on scenario characteristics, but when unconfounded external data are available, ATSE may result in less bias and improved precision compared to TSE and external control arm approaches. When external data are affected by unmeasured confounding, ATSE becomes prone to bias, but to a lesser extent compared to an external control arm approach.

翻译：肿瘤学领域的随机对照试验（RCTs）通常允许对照组受试者交叉接受实验性治疗，这种做法虽常出于伦理必要性，却使得长期治疗效应的准确估计变得复杂。当交叉率较高或样本量有限时，常用的交叉调整方法（如秩保持结构失效时间模型、逆概率删失加权法以及两阶段估计法）可能产生精度不足的估计结果。真实世界数据可用于构建RCT的外部对照组，但该方法忽略了未交叉受试者的证据，以及交叉受试者在交叉前所获数据的证据。本文提出“增强型两阶段估计”方法，该方法将RCT中未切换治疗的受试者数据与外部数据集相结合，形成一个“混合非切换组”。通过模拟研究，我们评估了ATSE方法与TSE交叉调整及外部对照组方法相比的性能。结果表明，性能表现取决于具体场景特征，但当可获得无混杂的外部数据时，与TSE和外部对照组方法相比，ATSE可能产生更小的偏倚和更高的估计精度。当外部数据受到未测量混杂因素影响时，ATSE易产生偏倚，但其程度低于外部对照组方法。