Pembrolizumab vs chemotherapy in the first-line setting of PD-L1≥50% metastatic non-small cell lung cancer: A real-world cost-effectiveness analysis.
Bibliographic record
Abstract
21 Background: Despite the voiced concerns around the initial economic decision models for first-line pembrolizumab in PD-L1≥50% metastatic non-small cell lung cancer (mNSCLC), there has been a paucity of evidence on the anticipated cost-effectiveness of pembrolizumab in the real world. To resolve the uncertainties associated with the initial assessment, we evaluated the real-world cost-effectiveness of first line pembrolizumab versus platinum-based chemotherapy for mNSCLC patients with PD-L1≥50%. Methods: We retrospectively identified a population-based cohort of mNSCLC patients who received first-line pembrolizumab or platinum-based chemotherapy between April 1, 2013, and March 31, 2021, in Ontario, Canada. Employing a public payer’s perspective, all costs (in Canadian dollars) and effects were estimated over a 4-year horizon, adjusted for censoring and discounted at 1.5% yearly. The primary outcomes were incremental cost-effectiveness ratios (ICERs) for life-years gained (LYG) and quality-adjusted life year (QALY). To examine the sensitivity of the ICER to drug acquisition costs and discounting, we conducted a price reduction analysis and a scenario analysis of different discount rates. Results: Propensity-score matching resulted in a total of 1,142 pairs of matched mNSCLC patients. Pembrolizumab extended survival with an incremental effect of 0.37 LYG and 0.35 QALY, but at an incremental cost of $56,681; the resulting ICERs were $154,941/LYG and $163,039/QALY. Though the ICERs were not sensitive to discounting rate, 31% and 55% reductions to the price of pembrolizumab brought the ICER below $100,000/QALY and $50,000/QALY, respectively. Conclusions: In the real world, first-line pembrolizumab is not considered to be cost-effective for mNSCLC patients with PD-L1≥50%. Improvements in cost-effectiveness, however, may be achievable through price renegotiations for pembrolizumab.
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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 | 0.043 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".