Associations between clinical benefits of cancer drugs and incremental quality-adjusted life years used in reimbursement decisions in Australia, Canada, England and China: an observational study
Bibliographic record
Abstract
OBJECTIVES: To investigate the association between incremental quality-adjusted life years (QALYs) predicted in economic evaluations and clinical benefits assessed by the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS), examining how accurately predicted QALYs reflect actual clinical outcomes in cancer drug reimbursement decisions. DESIGN: Cross-sectional observational study. SETTING: Health technology assessment (HTA) documents from Australia, Canada and England, supplemented by published economic evaluations from China. Economic evaluation data were collected from database inception to 31 December 2023. PARTICIPANTS: A total of 240 economic evaluation documents were identified from Australia (n=61), Canada (n=114) and England (n=65), along with 106 published studies from China, all focused on solid tumour cancer drugs with publicly available ESMO-MCBS scores. Documents were included based on completeness and consistency of data sources; those that were incomplete or relied on external controls were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcomes were the incremental QALYs from manufacturer submissions and HTA agency reevaluations. Secondary outcomes included associations stratified by data maturity, country, treatment setting and reimbursement recommendations. RESULTS: Incremental QALYs showed a moderate positive correlation with ESMO-MCBS scores (Spearman's ρ=0.42, 95% CI: 0.31 to 0.53). All country-specific correlations were statistically significant: England (ρ=0.53), Australia (ρ=0.37), Canada (ρ=0.39) and China (ρ=0.49), all p<0.01. Stronger associations were observed in HTA agency reevaluations (adjusted OR=1.43, 95% CI: 1.15 to 1.77) compared with manufacturer submissions (OR=1.21, 95% CI: 1.09 to 1.34). Analyses limited to mature data (>70% events observed) demonstrated the strongest association (OR=1.53, 95% CI: 1.10 to 2.13). Among countries, England exhibited the highest association (OR=1.42, 95% CI: 1.15 to 1.74), followed by China (OR=1.30, 95% CI: 1.04 to 1.62), Australia (OR=1.28, 95% CI: 1.01 to 1.63), and Canada (OR=1.15, 95% CI: 1.05 to 1.26). CONCLUSIONS: This study highlights a moderate alignment between incremental QALYs derived from economic evaluations and clinical benefit scores, emphasising the importance of rigorous reassessment, mature survival data and independent validation processes. Future research should explore strategies for enhancing data maturity and incorporating independent review mechanisms to strengthen healthcare decision-making globally.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".