COST‐EFFECTIVENESS AND COST‐UTILITY ANALYSIS OF MULTIPLE TREATMENT STRATEGIES USING ABVD AND/OR BEACOPP IN THE TREATMENT OF ADVANCED‐STAGE HODGKIN LYMPHOMA
Bibliographic record
Abstract
Background: There remains clinical equipoise over the best initial treatment strategy for advanced-stage Hodgkin lymphoma. Recent PET-adapted trials (RATHL, HD18, AHL2011) have tried to balance the trade-off that occurs with an inferior progression-free survival in the ABVD strategy, but higher rates of hematologic toxicity, infertility, and second malignancy in the BEACOPP strategy. The overall lifetime cost of each strategy remains unknown, especially costs which incorporate therapies for relapsed disease including brentuximab consolidation after autologous stem cell transplant and palliative therapy with nivolumab, and associated health state utilities. Methods: We developed a Markov decision analytic model to compare the life expectancy, quality-adjusted life expectancy (QALYs), and direct costs with varying upfront treatment regimens for a hypothetical cohort of transplant-eligible patients with newly-diagnosed advanced-stage Hodgkin lymphoma. A 20-year time horizon was used. Baseline probability estimates and utilities were derived from a systematic review of published studies (i.e. HD2000, Viviani, EORTC, HD15, HD18, RATHL, AHL2011, Echelon-1). A Canadian public health payer's perspective was considered and costs are presented in 2018 Canadian dollars. All costs and benefits were discounted by 1.5%. Sensitivity analyses were performed for key variables. Results: See Table 1 for results of the 20-year model. In the base-case analysis, the AHL2011 protocol was associated with both cost-savings and improved quality-adjusted outcomes over all other treatment strategies (Figure 1A). Sensitivity analyses demonstrated that the model was robust to key variables including probability of treatment-related mortality, probability of death from secondary malignancy, and probability of infertility secondary to BEACOPP. The threshold utility of infertility was found to be 0.71 (Figure 1B). Probabilistic sensitivity analyses (10,000 simulations) were performed (Figure 1C). For the WTP threshold of $100,000, AHL2011 was the dominant strategy 73% of the time (Figure 1D). Conclusions: The preferred treatment strategy for patients with newly diagnosed advanced-stage Hodgkin lymphoma is the AHL2011 PET-adapted regimen. This strategy maximizes life expectancy, quality-adjusted life years, and is the most cost-effective strategy, accounting for increased rates of hematologic toxicity, secondary malignancy, and infertility caused by exposure to at least 2 cycles of BEACOPP. Keywords: ABVD; BEACOPP; Hodgkin lymphoma (HL).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".