Challenges with integrating early-stage cancer trial endpoints into economic models: review of health technology recommendations for adjuvant or neoadjuvant therapies in Canada
Bibliographic record
Abstract
Adjuvant and neoadjuvant therapies for early-stage cancers demonstrate early clinical benefit in delaying disease recurrence. Health technology assessments require economic evaluations modeling lifetime disease trajectories. We examined modeling approaches used in Canadian health technology reviews to understand relevant challenges and identify opportunities for methodological improvements. From CADTH reimbursement recommendations for adjuvant/neo-adjuvant treatment of solid tumors, we collected outcomes and details of submitted clinical and economic evidence. We classified issues raised during economic review related to data maturity, surrogacy, treatment pathways, and assumptions surrounding extrapolation, duration of benefit and cure. Reviews from Jul/2015-Mar/2023 were identified. Reimbursement was recommended in 14/18 (78%) reviews. All assessments described OS as immature. Most (9/10, 90%) reviews with interim comparative OS data recommended reimbursement, while several (3/8, 38%) without OS data were not recommended. CADTH revisions changed implications for cost-effectiveness ($50,000/QALY threshold) in 10/18 (56%) reviews. Duration of benefit assumptions were inconsistent among both submitters and reviewers. Cure-time was consistently revised to ≥5 years from initiation. Despite surrogate endpoints and immature survival data, positive reimbursement recommendations were common. CADTH re-analyses frequently had modest impacts on cost-effectiveness. Further guidance is needed to capture benefits and assess uncertainties with more consistency for early-stage cancers.
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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 | 0.166 | 0.470 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".