Adopting life-cycle HTA: a tumor-agnostic precision oncology index economic evaluation from publicly available reimbursement reviews
Bibliographic record
Abstract
OBJECTIVES: Life-cycle health technology assessment (HTA) requires an index economic model to establish how estimated cost-effectiveness evolves with emerging evidence. We developed an open-source index economic evaluation of entrectinib, a tumor-agnostic therapy with conditional market authorization. Our objective was to replicate the initial HTA report from publicly available information, aiming to identify key operational and methodological aspects for operationalizing life-cycle decision-making. METHODS: We used partitioned survival analysis to determine tumor-agnostic and tumor-specific cost-effectiveness, using publicly available HTA reviews for parameterization. We estimated incremental costs in 2021 Canadian and US dollars (CAD and USD) from a public-payer healthcare perspective, quality-adjusted life years (QALYs), and incremental net monetary benefit (INMB). We assessed the impact of treatment effectiveness, extrapolation assumptions, and next-generation sequencing (NGS) costs. RESULTS: = 30) were unavailable in the Canadian reimbursement review and were sourced from international reviews. Tumor-agnostic incremental costs were CAD 68,451 (95 percent confidence interval: 35,466, 92,155) and USD 54,608 (28,294, 73,518), and QALYs were 0.13 (-0.42, 0.42), yielding INMB CAD -55,803 at 100,000/QALY (USD -44,518). Full extrapolation of treatment effectiveness also yielded negative INMB (CAD -66,664). Inclusion of NGS costs diminished the expected value. Heterogeneity was considerable across tumor indications. CONCLUSIONS: We developed an open-source index economic evaluation to operationalize life-cycle HTA for a conditionally authorized tumor-agnostic therapy. Our findings outline key operational and methodological considerations necessary for the development of index economic models that support life-cycle HTA, offering insights into their potential integration into regular HTA and policy decision-making processes.
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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 | 0.105 | 0.327 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".