Cost-effective analysis of the use of EGFR inhibitors (E) for wild-type (WT) KRAS unresectable metastatic colorectal cancer (mCRC).
Bibliographic record
Abstract
6552 Background: Patients (pts) with unresectable WT KRAS mCRC benefit from fluoropyrimidines (FP), oxaliplatin (O), irinotecan (I), bevacizumab (Bev) and E. The most cost-effective strategy to combine them remains unclear. Methods: A Markov model was constructed for a hypothetical cohort of pts with mCRC to examine the costs and outcomes of 3 treatment strategies: (A): 1st line (1L) Bev+FP+O/I, 2nd line (2L) FP+I/O, 3rd line (3L) E, (B): 1L Bev+FP+O/I, 2L FP+I/O, 3L E+I, and (C): 1L E+FP+O/I, 2L Bev+FP+I/O, 3L best supportive care (BSC). Efficacy and probability data of the treatments were obtained from clinical trials identified through a systematic review of the literature. Resource utilization data were derived from a chart review of 65 consecutive pts treated at Odette Cancer Centre (OCC) since 2009 and from the literature. Utilities were obtained by surveying oncologists (n= 24) across Canada using EQ-5D. Costs were obtained from the Ontario Ministry of Health and Long/Term Care, Ontario Case Costing Initiative, OCC and the literature. The analysis was conducted from the Canadian public healthcare system perspective over a 5 year time horizon with a 5% discount in 2012 Canadian dollars (CAD$) for cost and outcome. Incremental cost-effectiveness analyses were conducted comparing costs and outcomes of the 3 strategies. One way and probabilistic sensitivity analyses (SA) were conducted (n=10,000). Results: All 3 strategies appeared to be of relatively similar efficacy clinically, but C is more expensive than A or B by >45% (see Table). The model is primarily driven by the acquisition cost of drugs. B is most cost-effective when the willingness-to-pay (WTP) threshold >$120,000/QALY. SA showed that C would be cost-effective only when the progression-free survival of E is better than Bev in 1L with hazard ratio <0.24 at WTP of $150,000/QALY. Conclusions: 1L use of E followed by 2L Bev in mCRC is not cost-effective at the current pricing of E relative to Bev. [Table: see text]
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".