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 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".