Cost-Effectiveness Analyses, Costs and Resource Use, and Health-Related Quality of Life in Patients with Follicular or Marginal Zone Lymphoma: Systematic Reviews
Bibliographic record
Abstract
BACKGROUND: Follicular lymphoma (FL) and marginal zone lymphoma (MZL) are types of indolent non-Hodgkin lymphoma (NHL) that develop in the B lymphocytes (also known as B cells). OBJECTIVE: The aim of this study was to conduct a comprehensive review of studies relating to cost effectiveness, costs and resource use, and health-related quality of life (HRQoL) in patients with FL or MZL. METHODS: ), NHS EED, and EconLit databases. Select congress proceedings were also searched. Two systematic reviewers independently reviewed titles, abstracts, and full papers against eligibility criteria. Relevant data were extracted into bespoke data extraction templates (DETs) by a single systematic reviewer; these data were then validated for accuracy by a second reviewer against clean copies of the relevant publications. RESULTS: A total of 25 cost-effectiveness studies (24 in FL; 1 in FL and MZL) met the eligibility criteria. Markov models were the most utilised cost-effectiveness model. US FL studies reported an incremental cost-effectiveness ratio (ICER) of $28,565/QALY for first-line rituximab-cyclophosphamide, vincristine, and prednisone (R-CVP) versus CVP, and $43,000/QALY for second-line obinutuzumab plus bendamustine (G + B) followed by G maintenance versus B. In the UK, ICERs were £1529-10,834/quality-adjusted life-year (QALY) for first-line rituximab + chemotherapy versus chemotherapy, £27,988/QALY for second-line G + B + G-maintenance versus B, and £62,653/QALY for second-line idelalisib versus chemotherapy and/or rituximab. Five costs/resource use and four HRQoL studies were identified in FL, and none in MZL. US mean lifetime costs in first-line patients ranged from $108,000 (rituximab) to $130,300 (rituximab-cyclophosphamide, doxorubicin hydrochloride, vincristine and prednisolone [CHOP]), and from £2185 (watch-and-wait) to £17,054 (chemotherapy) in the UK. In a multinational study, more rituximab-refractory patients receiving G + B + G-maintenance reported a meaningful improvement in total FACT-Lym scores compared with patients receiving B. In the UK, total FACT-Lym scores were meaningfully higher for newly diagnosed patients compared with patients with progression (136.04 vs. 109.7). CONCLUSIONS AND RELEVANCE: We found a small body of evidence of quality of life, and potentially cost-effective treatment options for FL; however, no evidence was reported on MZL specifically. The significant data gaps in knowledge in these diseases demonstrate a marked need for further studies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 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".