BACKGROUND Long-term clinical outcomes are necessary to assess the cost-effectiveness of new treatments over a lifetime horizon. Without long-term clinical trial data, current practice to extrapolate survival beyond the trial period involves fitting alternative parametric models to the observed survival. Choosing the most appropriate model is based on how well each model fits to the observed data. Supplementing trial data with feedback from experts may improve the plausibility of survival extrapolations. We demonstrate the feasibility of formally integrating long-term survival estimates from experts with empirical clinical trial data to provide more credible extrapolated survival curves. METHODS The case study involved relapsed or refractory B-cell pediatric and young adult acute lymphoblastic leukemia (r/r pALL) regarding long-term survival for tisagenlecleucel (chimeric antigen receptor T-cell [CAR-T]) with evidence from the phase II ELIANA trial. Seven pediatric oncologists and hematologists experienced with CAR-T therapies were recruited. Relevant evidence regarding r/r pALL and tisagenlecleucel provided a common basis for expert judgments. Survival rates and related uncertainty at 2, 3, 4, and 5 years were elicited from experts using a web-based application adapted from Sheffield Elicitation Framework. Estimates from each expert were combined with observed data using time-to-event parametric models that accounted for experts' uncertainty, producing an overall distribution of survival over time. These results were validated based on longer term follow-up (median duration 24.2 months) from ELIANA following the elicitation. RESULTS Extrapolated survival curves based on ELIANA trial without expert information were highly uncertain, differing substantially depending on the model choice. Survival estimates between 2 to 5 years from individual experts varied with a fair amount of uncertainty. However, incorporating expert estimates improved the precision in the extrapolated survival curves. Predictions from a Gompertz model, which experts believed was most appropriate, suggested that more than half of the ELIANA patients treated with tisagenlecleucel will survive up to 5 years. Expert estimates at 24 months were validated by longer follow-up. CONCLUSIONS This study provides an example of how expert opinion can be elicited and synthesized with observed survival data using a transparent and formal procedure, capturing expert uncertainty, and ensuring projected long-term survival is clinically plausible.

中文翻译：

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将专家意见与临床试验数据相结合以推断长期生存：针对患有复发或难治性急性淋巴细胞白血病的儿童和年轻人的 CAR-T 治疗案例研究。

背景 长期临床结果对于评估新疗法在一生中的成本效益是必要的。在没有长期临床试验数据的情况下，当前推断试验期后生存的实践涉及将替代参数模型拟合到观察到的生存。选择最合适的模型取决于每个模型与观测数据的拟合程度。用专家的反馈补充试验数据可能会提高生存外推的合理性。我们证明了将专家的长期生存估计与经验临床试验数据正式整合以提供更可信的外推生存曲线的可行性。方法 该案例研究涉及复发或难治性 B 细胞儿童和年轻成人急性淋巴细胞白血病 (r/r pALL)，涉及 tisagenlecleucel（嵌合抗原受体 T 细胞 [CAR-T]）的长期生存，并具有 II 期证据埃利亚娜审判。招募了 7 名具有 CAR-T 疗法经验的儿科肿瘤学家和血液学家。r/r pALL 和 tisagenlecleucel 的相关证据为专家判断提供了共同基础。专家使用改编自谢菲尔德启发框架的基于网络的应用程序得出了 2 年、3 年、4 年和 5 年的生存率和相关不确定性。使用事件发生时间参数模型将每位专家的估计与观察到的数据相结合，该模型考虑了专家的不确定性，从而产生了随时间变化的总体生存分布。这些结果根据 ELIANA 诱导后的长期随访（中位持续时间 24.2 个月）进行了验证。结果 根据没有专家信息的 ELIANA 试验推断的生存曲线高度不确定，根据模型选择的不同而有很大差异。个别专家对 2 至 5 年生存率的估计各不相同，存在相当大的不确定性。然而，结合专家的估计提高了外推生存曲线的精度。专家认为最合适的 Gompertz 模型预测表明，接受 tisagenlecleucel 治疗的 ELIANA 患者中一半以上将存活长达 5 年。专家对 24 个月的估计通过更长时间的随访得到验证。结论 这项研究提供了一个例子，说明如何使用透明和正式的程序，利用观察到的生存数据来引出和综合专家意见，捕获专家的不确定性，并确保预计的长期生存在临床上合理。