Frontline rituximab monotherapy induction versus a watch and wait approach for asymptomatic advanced‐stage follicular lymphoma: A cost‐effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: A watch and wait (WW) strategy is the standard of care for patients with asymptomatic advanced-stage follicular lymphoma. Recent data have demonstrated an improvement in the time to progression with rituximab induction (RI) with or without rituximab maintenance (RM) in comparison with a WW strategy wait in such patients. It remains unclear whether this is a cost-effective strategy. METHODS: A Markov decision analysis model was developed to compare the clinical outcomes, costs, and cost-effectiveness of RI (4 weekly doses) plus RM (12 doses every 2 months), RI (4 weekly doses), and a WW strategy for patients newly diagnosed with low-burden, asymptomatic advanced-stage follicular lymphoma over a lifetime horizon. Baseline probabilities and utilities were derived from a systematic review of published studies, and they were evaluated on a 6-month cycle. A Canadian public health payer's perspective was adopted, and costs were presented in 2012 Canadian dollars. RESULTS: RI was the cheapest strategy. It was less costly at $59,953 versus $67,489 for the RM arm and $75,895 for the WW arm. It was also associated with a slightly lower quality-adjusted life expectancy at 6.16 quality-adjusted life years (QALYs) versus 6.28 QALYs for the RM strategy but was superior to WW (5.71 QALYs). In sensitivity analyses of key variables, this effectiveness was sensitive to the probability of first and second progression in the RI arm, and this indicated relatively neutral effectiveness between the 2 rituximab arms. CONCLUSIONS: RI without maintenance for asymptomatic advanced-stage follicular lymphoma is the preferred strategy: it minimizes costs per patient over a lifetime horizon.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".