Rapid Review of Real-World Cost-Effectiveness Analyses of Cancer Interventions in Canada
Bibliographic record
Abstract
Cost-effectiveness analysis (CE Analysis) provides evidence about the incremental gains in patient outcomes costs from new treatments and interventions in cancer care. The utilization of "real-world" data allows these analyses to better reflect differences in costs and effects for actual patient populations with comorbidities and a range of ages as opposed to randomized controlled trials, which use a restricted population. This rapid review was done through PubMed and Google Scholar in July 2022. Relevant articles were summarized and data extracted to summarize changes in costs (in 2022 CAD) and effectiveness in cancer care once funded by the Canadian government payer system. We conducted statistical analyses to examine the differences between means and medians of costs, effects, and incremental cost effectiveness ratios (ICERs). Twenty-two studies were selected for review. Of those, the majority performed a CE Analysis on cancer drugs. Real-world cancer drug studies had significantly higher costs and effects than non-drug therapies. Studies that utilized a model to project longer time-horizons saw significantly smaller ICER values for the treatments they examined. Further, differences in drug costs increased over time. This review highlights the importance of performing real-world CE Analysis on cancer treatments to better understand their costs and impacts on a general patient population.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".