Cost-effectiveness of tositumomab and iodine I-131 tositumomab (Bexxar therapeutic regimen (BTR)), in treatment of non- Hodgkin lymphoma (NHL)
Bibliographic record
Abstract
8089 Background: BTR has demonstrated efficacy in NHL patients and it has potential to prolong time to progression (TTP) in relapsed/refractory, low grade, follicular, or transformed NHL. This study assessed effectiveness and cost-effectiveness of BTR compared to alternative therapies in first, second, and third line NHL therapy. Methods: Time-to-event models were constructed with 2 events: progression and death. Patient data from 8 BTR clinical trials were combined to fit Weibull models for TTP and overall survival (OS) by including FLIPI covariates. Estimates for BTR were compared with estimates for alternatives from Weibull models fitted to published TTP and BTR OS data by lines of therapy and measured in life-years (LY). Estimated pre-progression costs included drug costs, lab tests, monitoring, and adverse events; post progression costs included NHL costs until death, all valued in 2006 $US and discounted at 3%. Indirect comparisons yielded incremental cost-effectiveness ratios (ICER=Δ cost/Δ LYs) in each line of therapy. Results: As observed in the table , cost of care estimates in BTR were often comparable with alternative therapies, but typically LY gain favored BTR. Mostly in first and third line, a BTR strategy had an ICER less than the cost-effectiveness threshold of $50,000 per LY gained. Conclusion: Overall, a BTR strategy has a favorable cost-effectiveness profile to alternative strategies including rituximab maintenance (RXM) in first, second, and third line NHL therapy. Results imply both a possible survival gain with early BTR use, and the cost-effectiveness of BTR. This modeling approach can aid in clinical decision making regarding the sequence and timing of therapy for patients with follicular NHL. No significant financial relationships to disclose. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".