Cost-effectiveness of pembrolizumab in second-line advanced bladder cancer.
Bibliographic record
Abstract
472 Background: Recently, immune-modulating drugs have been introduced to the second line setting of advanced bladder cancer. Pembrolizumab increases overall survival and is associated with less toxicity compared to chemotherapy in this setting based on the Keynote-045 study. The high cost of immunotherapy necessitates an assessment of its value by considering both efficacy and cost. The objective of this study is to estimate the cost-effectiveness of pembrolizumab for the second-line treatment of advanced bladder cancer from the perspective of payers in multiple countries. Methods: We developed a Markov model to compare the costs and effectiveness of pembrolizumab with those of chemotherapy in the second-line treatment of advanced bladder cancer. Health outcomes were measured in life-years (LYs) and quality-adjusted life-years (QALYs). Drug costs were acquired for the following countries: U.S., U.K., Canada and Australia. Model robustness was addressed in univariate and probabilistic sensitivity analyses. Results: Pembrolizumab generated a gain of 0.36 QALYs compared to chemotherapy. Our analysis established the following incremental cost-effectiveness ratios (ICERs) for pembrolizumab versus chemotherapy in second-line advanced bladder cancer treatment - U.S. $122,557/QALY, U.K. $91,995/QALY, Canada $93,648/QALY, and Australia $99,966/QALY. The willingness-to-pay (WTP) thresholds per QALY are considered to be around 100,000-150,000 US dollars for the U.S., 20,000-50,000 pounds for the U.K. [25,000-65,000 US $], 20,000-100,000 CAD for Canada [16,000-80,000 US $] and 40,000-75,000 AUD for Australia [32,000-60,000 US $]. Conclusions: Cost-effectiveness and WTP thresholds vary between countries. Compared to the other countries examined, U.S. drug prices were found to be highest, leading to the highest ICER. With standard willingness-to-pay thresholds, pembrolizumab may be considered cost-effective in the U.S., but not in the other countries examined.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".