BMC Medical Research Methodology (Sep 2019)
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
Abstract
Abstract 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.
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