Health Technology Reassessment: Addressing Uncertainty in Economic Evaluations of Oncology Drugs at Time of Reimbursement Using Long-Term Clinical Trial Data
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.165 | 0.359 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".