Cost-effectiveness of chimeric antigen receptor T-cell therapy for relapsed or refractory large B-cell lymphoma: a systematic review and meta-analysis
Bibliographic record
Abstract
Aims Cost-effectiveness evidence of chimeric antigen receptor T-cell therapy (CAR-T) for the treatment of relapsed or refractory large B-cell lymphoma (r/r LBCL) remains controversial given the potential CAR-T has to cure patients coupled with the high cost of therapy. This study aims to synthesize the cost-effectiveness data of CAR-T for the treatment of r/r LBCL in adults using a systematic review and meta-analysis.Methods Cost-effectiveness analyses of CAR-T for the treatment of r/r LBCL from inception through November 2024 were identified through database searches (MEDLINE, EMBASE, and CENTRAL). Costs were converted to 2023 US dollars using purchasing power parity. Incremental net benefit (INB) was calculated using country-specific willingness-to-pay thresholds and pooled using a random-effects model. Results were stratified by country income level and line of therapy. The risk of bias was assessed using the ECOBIAS checklist. Protocol registered at PROSPERO #CRD42024602683.Results Thirty-four studies were included in this systematic review. Twenty-six studies compared CAR-T to platinum-based chemotherapy with autologous stem cell therapy (ASCT) for r/r LBCL and eight studies compared between CAR-T therapies. As 2nd-line therapy for LBCL, CAR-T was found to have an INB of $28,846 (95% Confidence Interval [CI]: −$43,265 to $100,957; I2 = 0%) (n = 9) compared to platinum-based chemotherapy with ASCT in high-income countries (HICs). As 3rd-line therapy against the same comparator, CAR-T had an INB of $48,838 (95% CI: −$156,625 to $254,302; I2 = 79%) (n = 9) for HICs.Conclusions Over a lifetime horizon, CAR-T was not significantly cost-effective for the treatment of r/r LBCL, though positive INBs suggest a trend toward cost-effectiveness as 2nd or 3rd-line therapy in HICs. Further cost-effective analyses should be conducted to compare between CAR-T products for the treatment of patients with r/r LBCL, as well as between CAR-T products and emerging novel therapies for r/r LBCL treatment, as this study did not address these comparisons.
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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".