Real-World Cost Effectiveness of a Policy of KRAS Testing to Inform Cetuximab or Panitumumab for Third-Line Therapy of Metastatic Colorectal Cancer in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Cetuximab and panitumumab, two anti-EGFR therapies, are widely used for third-line therapy of metastatic colorectal cancer (mCRC) with wild-type KRAS, but there remains uncertainty around their cost effectiveness. The objective of this analysis was to conduct a real-world cost-effectiveness analysis of the policy change introducing KRAS testing and third-line anti-EGFR therapy mCRC in British Columbia (BC), Canada. METHODS: We conducted secondary analysis of administrative data for a cohort of mCRC patients treated in BC in 2006-2015. Patients potentially eligible for KRAS testing and third-line therapy after the policy change (July 2009) were matched 2:1 to pre-policy patients using genetic matching on propensity score and baseline covariates. Costs and survival time were calculated over an 8-year time horizon, with bootstrapping to characterize uncertainty around endpoints. Cost effectiveness was expressed using incremental cost-effectiveness ratios (ICER) and the probability of cost effectiveness at a range of thresholds. RESULTS: The cohort included 1757 mCRC patients (n = 456 pre-policy and n = 1304 post-policy; of those, n = 420 received cetuximab or panitumumab). There was a significant increase in survival and cost following the policy change. Adoption of KRAS testing and anti-EGFR therapy had an ICER of CA$73,759 per life-year gained (LYG) (95% CI 46,133-186,446). In scenario analysis, a reduction in cetuximab and panitumumab cost of at least 50% was required to make the policy change cost effective at a threshold of CA$50,000/LYG. CONCLUSION: A policy of third-line anti-EGFR therapy informed by KRAS testing may be considered cost effective at thresholds above CA$70,000/LYG. Reduction in drug costs, through price discounts or potential future biosimilars, would make anti-EGFR therapy considerably more cost effective. By using real-world data for a large cohort with long follow-up we can assess the value of a policy of KRAS testing and anti-EGFR therapy achieved in practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".