Cost‐effectiveness of second‐line ipilimumab for metastatic melanoma: A real‐world population‐based cohort study of resource utilization
Bibliographic record
Abstract
BACKGROUND: The efficacy-effectiveness gap between randomized trial and real-world evidence regarding the clinical benefit of ipilimumab for metastatic melanoma (MM) has been well characterized by previous literature, consistent with initial concerns raised by health technology assessment agencies (HTAs). As these differences can significantly impact cost-effectiveness, it is critical to assess the real-world cost-effectiveness of second-line ipilimumab versus non-ipilimumab treatments for MM. METHODS: This was a population-based retrospective cohort study of patients who received second-line non-ipilimumab therapies between 2008 and 2012 versus ipilimumab treatment between 2012 and 2015 (after public reimbursement) for MM in Ontario. Using a 5-year time horizon, censor-adjusted and discounted (1.5%) costs (from the public payer's perspective in Canadian dollars) and effectiveness were used to calculate incremental cost-effectiveness ratios (ICERs) in life-years gained (LYGs) and quality-adjusted life years (QALYs), with bootstrapping to capture uncertainty. Varying the discount rate and reducing the price of ipilimumab were done as sensitivity analyses. RESULTS: In total, 329 MM were identified (Treated: 189; Controls: 140). Ipilimumab was associated with an incremental effectiveness of 0.59 LYG, incremental cost of $91,233, and ICER of $153,778/LYG. ICERs were not sensitive to discounting rate. Adjusting for quality of life using utility weights resulted in an ICER of $225,885/QALY, confirming the original HTA estimate prior to public reimbursement. Reducing the price of ipilimumab by 100% resulted in an ICER of $111,728/QALY. CONCLUSION: Despite its clinical benefit, ipilimumab as second-line monotherapy for MM patients is not cost-effective in the real world as projected by HTA under conventional willingness-to-pay thresholds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".