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首页> 外文期刊>BMC Medical Research Methodology >Integrating expert opinion with clinical trial data to extrapolate long-term survival: a case study of CAR-T therapy for children and young adults with relapsed or refractory acute lymphoblastic leukemia
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Integrating expert opinion with clinical trial data to extrapolate long-term survival: a case study of CAR-T therapy for children and young adults with relapsed or refractory acute lymphoblastic leukemia

机译：将专家意见与临床试验数据相结合以推断长期生存：CAR-T疗法对复发性或难治性急性淋巴细胞白血病的儿童和青少年的案例研究

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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.
机译：长期的临床结果对于评估一生中新疗法的成本效果是必要的。由于没有长期的临床试验数据，目前的推断试验期后生存率的实践涉及将替代参数模型拟合到观察到的生存率。选择最合适的模型是基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以改善生存推断的合理性。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靠的推断生存曲线的可行性。病例研究涉及复发或难治性B细胞小儿和年轻成人急性淋巴细胞白血病（r / r pALL），有关替沙可核糖核酸（嵌合抗原受体T细胞[CAR-T]）的长期生存，证据来自II期ELIANA试用。招募了7位具有CAR-T疗法经验的儿科肿瘤学家和血液学家。有关r / r pALL和tisagenlecleucel的相关证据为专家判断提供了共同基础。通过改编自Sheffield Elicitation Framework的基于Web的应用程序，专家得出了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值与观察到的数据相结合，这些参数模型可以解释专家的不确定性，从而得出生存率随时间的总体分布。这些结果是根据诱发后ELIANA的长期随访（中位持续时间24.2个月）进行验证的。在没有专家信息的情况下，基于ELIANA试验得出的推断生存曲线非常不确定，具体取决于模型选择。各个专家在2到5年之间的生存估计存在很大的不确定性。但是，合并专家估计可以提高推断生存曲线的精度。专家认为最合适的Gompertz模型预测结果表明，超过一半的接受tisagenlecleucel治疗的ELIANA患者可以存活5年。经过24个月的随访，专家评估得到了证实。这项研究提供了一个示例，说明了如何使用透明和正式的程序来得出专家意见并与观察到的生存数据进行综合，捕获专家的不确定性，并确保预计的长期生存在临床上可行。

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