Abstract
BackgroundLong-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.MethodsThe 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.ResultsExtrapolated 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.ConclusionsThis 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.
Highlights
Decision-makers need to understand long-term clinical outcomes to assess cost-effectiveness of new treatments over a lifetime horizon
This study provides an example of how expert opinion can be elicited and combined with observed survival data from trials in a transparent, formal, and reproducible manner, to ensure that projected long-term survival can be integrated in cost-effectiveness models and is clinical plausible
This method provides a meaningful improvement over the standard approaches to incorporate expert opinion in cost-effectiveness modeling, which frequently involves a post-hoc validation of extrapolated survival curves by a single expert
Summary
Decision-makers need to understand long-term clinical outcomes to assess cost-effectiveness of new treatments over a lifetime horizon. In the absence of long-term data from clinical trials, current practice to extrapolate observed survival data beyond the clinical trial follow-up period typically involves fitting alternative parametric models to the observed survival. Despite the sensitivity of cost-effectiveness estimates to extrapolation, conventional cost-effectiveness models typically do not explicitly ‘consider external long-term validity’ [2] of extrapolations from clinical data. 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. 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
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