Economic Evaluations of Chimeric Antigen Receptor T-Cell Therapies for Hematologic and Solid Malignancies: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: This study aimed to systematically review evidence on the cost-effectiveness of chimeric antigen receptor T-cell (CAR-T) therapies for patients with cancer. METHODS: Electronic databases were searched in October 2022 and updated in September 2023. Systematic reviews, health technology assessments, and economic evaluations that compared costs and effects of CAR-T therapy in patients with cancer were included. Two reviewers independently screened studies, extracted data, synthesized results, and critically appraised studies using the Philips checklist. Cost data were presented in 2022 US dollars. RESULTS: Our search yielded 1809 records, 47 of which were included. Most of included studies were cost-utility analysis, published between 2018 and 2023, and conducted in the United States. Tisagenlecleucel, axicabtagene ciloleucel, idecabtagene vicleucel, ciltacabtagene autoleucel, lisocabtagene maraleucel, brexucabtagene autoleucel, and relmacabtagene autoleucel were compared with various standard of care chemotherapies. The incremental cost-effectiveness ratio (ICER) for CAR-T therapies ranged from $9424 to $4 124 105 per quality-adjusted life-year (QALY) in adults and from $20 784 to $243 177 per QALY in pediatric patients. Incremental cost-effectiveness ratios were found to improve over longer time horizons or when an earlier cure point was assumed. Most studies failed to meet the Philips checklist due to a lack of head-to-head comparisons and uncertainty surrounding CAR-T costs and curative effects. CONCLUSIONS: CAR-T therapies were more expensive and generated more QALYs than comparators, but their cost-effectiveness was uncertain and dependent on patient population, cancer type, and model assumptions. This highlights the need for more nuanced economic evaluations and continued research to better understand the value of CAR-T therapies in diverse patient populations.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".