Abstract
The evidence base to support reimbursement decision making for oncology drugs is often based on short-term follow-up trial data, and attempts to address this uncertainty are not typically undertaken once a reimbursement decision is made. To address this gap, we sought to conduct a reassessment of an oncology drug (pembrolizumab) for patients with advanced melanoma which was approved based on interim data with a median 7.9 months of follow-up and for which long-term data have since been published. We developed a three-health-state partitioned survival model based on the phase 3 KEYNOTE-006 clinical trial data using patient-level data reconstruction techniques based on an interim analysis. We used a standard survival analysis and parametric curve fitting techniques to extrapolate beyond the trial follow-up time, and the model structure and inputs were derived from the literature. Five-year long-term follow-up data from the trial were then used to re-evaluate the cost-effectiveness of pembrolizumab versus ipilimumab for treatment of advanced melanoma. The best fitting parametric curves and corresponding survival extrapolations for reconstructed interim data and long-term data reconstructed from KEYNOTE-006 were different. An analysis of the 5 year long-term follow-up data generated a base case incremental cost-effectiveness ratio (ICER) that was 28% higher than the ICER based on interim trial data. Our findings suggest that there may be a trade-off between certainty and the ICER. Conducting health technology re-assessments of certain oncology products on the basis of longer-term data availability, especially for those health technology adoption decisions made based on immature clinical data, may be of value to decision makers.