The Impact of Methodological Approach on Cost Findings in Comparison of Epoetin Alfa with Darbepoetin Alfa
Bibliographic record
Abstract
BACKGROUND: Two erythropoiesis-stimulating agents (ESAs), epoetin alfa and darbepoetin alfa, are approved for the treatment of chemotherapy-induced anemia in patients with cancer. Randomized controlled trials indicate that the drugs are similarly efficacious, but that the duration of clinical benefit (DCB) ranges from 2 to 7 days for epoetin alfa and from 7 to 21 days for darbepoetin alfa, depending on dose. Given equivalent efficacy, payers are increasingly interested in understanding the cost differences for these 2 drugs. OBJECTIVE: To examine the impact of different methodological approaches on the cost comparison between epoetin alfa and darbepoetin alfa users, with cancer from a payer perspective. METHODS: Episodes of care (episode) were constructed for cancer patients treated with ESAs, using MarketScan claims data. Episodes started with the first ESA claim and ended on the last ESA claim or the claim before a 42-day or longer gap in ESA therapy. Each episode was augmented with an estimated DCB based on the last dose in the episode. Cost was reimbursed amount observed in the claims database. Adjusted weekly cost was estimated using generalized linear models to control for difference in clinical and demographic differences across epoetin alfa and darbepoetin alfa episodes. RESULTS: Episodes were created in 324 darbepoetin alfa and 342 epoetin alfa users. Darbepoetin alfa users tended to be younger, had more comorbidities, and had advanced cancer (all p < 0.001). After accounting for DCB, the average weekly cost of darbepoetin alfa was significantly lower than that of epoetin alfa ($619 vs $940; p < 0.001). After multivariate adjustment, darbepoetin alfa had lower costs than epoetin alfa in the base case and all alternative approaches. CONCLUSIONS: To reduce the risk of potential bias, DCB and different patient characteristics should be taken into account when using retrospective claims data to conduct cost comparisons between agents that have significant differences in dosing schedule.
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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 | 0.557 | 0.800 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.015 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".