When is Cancer Care Cost-Effective? A Systematic Overview of Cost–Utility Analyses in Oncology
Bibliographic record
Abstract
New cancer treatments pose a substantial financial burden on health-care systems, insurers, patients, and society. Cost-utility analyses (CUAs) of cancer-related interventions have received increased attention in the medical literature and are being used to inform reimbursement decisions in many health-care systems. We identified and reviewed 242 cancer-related CUAs published through 2007 and included in the Tufts Medical Center Cost-Effectiveness Analysis Registry (www.cearegistry.org). Leading cancer types studied were breast (36% of studies), colorectal (12%), and hematologic cancers (10%). Studies have examined interventions for tertiary prevention (73% of studies), secondary prevention (19%), and primary prevention (8%). We present league tables by disease categories that consist of a description of the intervention, its comparator, the target population, and the incremental cost-effectiveness ratio. The median reported incremental cost-effectiveness ratios (in 2008 US $) were $27,000 for breast cancer, $22,000 for colorectal cancer, $34,500 for prostate cancer, $32,000 for lung cancer, and $48,000 for hematologic cancers. The results highlight the many opportunities for efficient investment in cancer care across different cancer types and interventions and the many investments that are inefficient. Because we found only modest improvement in the quality of studies, we suggest that journals provide specific guidance for reporting CUA and assure that authors adhere to guidelines for conducting and reporting economic evaluations.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".