Cost–effectiveness of ropeginterferon alfa-2b-njft for the treatment of polycythemia vera
Bibliographic record
Abstract
Aim: Patients with polycythemia vera (PV), a rare and chronic blood cancer, are at a higher risk for thromboembolic events, progression to myelofibrosis, and leukemic transformation. In 2021, ropeginterferon alfa-2b-njft (BESREMi ® ) was approved in the US to treat adults with PV. The purpose of this study is to estimate the cost–effectiveness of ropeginterferon alfa-2b-njft, used as a first- or second-line treatment, for the treatment of patients with PV in the US. Materials & methods: A Markov cohort model was developed from the healthcare system perspective in the United States. Model inputs were informed by the PROUD-PV and CONTINUATION-PV studies and published literature. The model population included both low-risk and high-risk patients with PV. The model compared ropeginterferon alfa-2b-njft used either as first- or second-line versus an alternative treatment pathway of first-line hydroxyurea followed by ruxolitinib. Results: Over the modeled lifetime, ropeginterferon alfa-2b-njft provided an additional 0.4 higher quality-adjusted life years (QALYs) and 0.4 life-years with an added cost of USD60,175, resulting in a cost per QALY of USD141,783. The model was sensitive to treatment costs, the percentage of patients who discontinue hydroxyurea, the percentage of ropeginterferon alfa-2b-njft users who switch to monthly dosing, the percentage of ropeginterferon alfa-2b-njft users as 2nd line treatment, and the treatment response rates. A younger patient age at baseline and a higher percentage of patients with low-risk disease improved the cost–effectiveness of ropeginterferon alfa-2b-njft. Conclusion: Ropeginterferon alfa-2b-njft is a cost-effective treatment option for a broad range of patients with PV, including both low- and high-risk patients and patients with and without prior cytoreductive treatment with hydroxyurea.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".