Cost implications of new treatments for advanced colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Since 1996, 6 new drugs have been introduced for the treatment of metastatic colorectal cancer. Although they are promising, these drugs frequently are given in the palliative and are much more expensive than older treatments. The objective of the current study was to measure the cost implications of treatment with sequential regimens that include chemotherapy and/or monoclonal antibodies. METHODS: A Markov model was used to evaluate a hypothetical cohort of 1000 patients with newly diagnosed, metastatic colorectal cancer. Patients supposedly received up to 3 lines of treatment before supportive care and subsequent death. Data were obtained from published, multicenter phase 2 and randomized phase 3 clinical trials. Sensitivity analyses were conducted on the efficacy, toxicity, and cost. RESULTS: Using drug costs alone, treatment that included new chemotherapeutic agents increased survival at an incremental cost-effectiveness ratio (ICER) of $100,000 per discounted life-year (DLY). The addition of monoclonal antibodies improved survival at an ICER of >$170,000 per DLY. The results were most sensitive to changes in the initial regimen. Even with significant improvements in clinical characteristics (efficacy and toxicity), treatment with the most effective regimens still had very high ICERs. CONCLUSIONS: Treatment of metastatic colorectal cancer with the most effective regimens came at very high incremental costs. The authors concluded that cost-effectiveness analyses should be a routine component of the drug-development process, so that physicians and patients are informed appropriately regarding the value of new innovations.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".