Precision Xtract, 1505 West 2nd Avenue, Suite 300, Vancouver, BC, V6H 3Y4, Canada.
Global Oncology Strategy and Business Dev, Novartis Pharmaceuticals Corporation, 1 Health Plaza, East Hanover, NJ, 07936, USA.
BMC Med Res Methodol. 2019 Sep 2;19(1):182. doi: 10.1186/s12874-019-0823-8.
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.
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.
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.
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.
为了评估新疗法在整个生命周期内的成本效益,需要长期的临床结果。在没有长期临床试验数据的情况下,目前通过将替代参数模型拟合到观察到的生存数据来推断试验期间之外的生存情况。选择最合适的模型取决于每个模型与观察数据的拟合程度。通过从专家那里获得反馈来补充试验数据,可能会提高生存推断的合理性。我们展示了如何正式将专家提供的长期生存估计与经验性临床试验数据相结合,以提供更可信的外推生存曲线。
该案例研究涉及复发或难治性 B 细胞儿科和年轻成人急性淋巴细胞白血病(r/r pALL)的长期生存情况,涉及 tisagenlecleucel(嵌合抗原受体 T 细胞 [CAR-T]),其证据来自 II 期 ELIANA 试验。招募了 7 位具有 CAR-T 治疗经验的儿科肿瘤学家和血液学家。关于 r/r pALL 和 tisagenlecleucel 的相关证据为专家判断提供了共同基础。使用从谢菲尔德 elicitation framework 改编的网络应用程序,从专家那里获得了 2、3、4 和 5 年的生存率和相关不确定性。使用考虑到专家不确定性的时间事件参数模型,从每位专家那里综合获得的估计值与观察数据相结合,从而生成随时间变化的总体生存分布。这些结果基于 ELIANA 之后的更长随访时间(中位随访时间 24.2 个月)进行了验证。
基于 ELIANA 试验没有专家信息的外推生存曲线高度不确定,模型选择有很大差异。个别专家在 2 至 5 年内的生存估计存在很大的不确定性。但是,纳入专家估计值可提高外推生存曲线的精度。专家认为最适合的 Gompertz 模型的预测表明,接受 tisagenlecleucel 治疗的 ELIANA 患者中有一半以上将存活至 5 年。更长的随访时间验证了 24 个月时的专家估计值。
本研究提供了一个示例,说明如何通过透明和正式的程序,使用观察到的生存数据引出和综合专家意见,同时捕获专家不确定性,并确保预测的长期生存是合理的。