Use of the ESMO-Magnitude of Clinical Benefit Scale to guide HTA recommendations on coverage and reimbursement for cancer medicines: a retrospective analysis
Bibliographic record
Abstract
Background Recommendations by countries' health technology assessment (HTA) agencies are used to decide which new therapies warrant the allocation of limited health-care resources to make them available through publicly funded health systems. This process is of public health importance for balancing the dual aims of optimising patient outcomes while ensuring financial sustainability. We evaluated which factors affect HTA outcomes and the time to positive HTA outcome, focussing on the role of clinical benefit evaluated with the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS). Methods In this retrospective analysis, data were extracted from publicly available HTA reports and related sources from six country settings and their respective HTA agencies (Australia, Canada, England, France, the Canadian province of Quebec, and Scotland). We evaluated new cancer medicines for treating solid tumours in a non-curative setting with published ESMO-MCBS scores and that had been assessed by at least three HTA agencies between Jan 1, 2011, and Dec 31, 2020. Using ESMO-MCBS score as an independent variable, we did descriptive and multivariable regression analyses to evaluate: (1) factors associated with the time between marketing authorisation and positive (unrestricted [List] and restricted [List with Constraints]) HTA outcome; and (2) factors associated with HTA outcomes. Findings 67 medicine–indication pairs used in non-curative settings were identified, totalling 360 HTA submissions (medicine–indication–country triplets) reviewed by the six HTA agencies. Factors significantly associated with a reduced interval between marketing authorisation and a positive (unrestricted or restricted) HTA outcome included a high ESMO-MCBS score (ie, 4 or 5, vs a low or average score of 1–3; hazard ratio [HR] per 1 month increment 1·42 [95% CI 1·11–1·81], p=0·0055), parallel review ( vs standard marketing authorisation process; HR 1·69 [1·13–2·54], p=0·011), having a risk-sharing agreement or special funding arrangements ( vs no funding agreement, HR 4·62 [95% CI 2·51–8·51], p<0·0001, and HR 4·16 [2·03–8·50], p=0·0001, respectively), and assessment by particular HTA agencies (pan-Canadian Oncology Drug Review vs National Institute for Health and Care Excellence [NICE], HR 2·82 [1·68–4·75], p=0·0001; and Haute Autorité de Santé vs NICE, HR 5·70 [2·87–11·33], p<0·0001). Accelerated marketing authorisation was significantly associated with a longer time to positive HTA outcome ( vs standard authorisation process; HR 0·70 [95% CI 0·51–0·95], p=0·024). Positive HTA outcomes (both unrestricted and restricted) were significantly associated with a high ESMO-MCBS score ( vs low or average ESMO-MCBS score; relative risk ratio [RRR] 14·10 [95% CI 3·54–56·20], p=0·0002, and RRR 4·52 [1·90–10·75], p=0·0006, respectively) and acknowledgment of unmet medical need ( vs unmet need not recorded, RRR 22·73 [5·51–93·73], p<0·0001, and RRR 1·87 [1·18–2·97], p=0·0075, respectively). By contrast, positive HTA outcomes (unrestricted and restricted) were inversely associated with uncertainties regarding inputs to economic models informing HTA submissions ( vs uncertainties not recorded, RRR 0·28 [0·10–0·78], p=0·014, and RRR 0·45 [0·25–0·82], p=0·010, respectively). Regarding country-relevant effects, inverse associations with positive HTA outcomes (both unrestricted and restricted) were observed for assessment in Quebec ( vs England; RRR 1·15×10 −6 [1·44×10 −7 –9·09×10 −6 ], p<0·0001, and RRR 0·33 (0·24–0·46), p<0·0001, respectively) and for assessment in Australia ( vs England; RRR 1·78×10 −6 [1·04×10 −8 –3·00×10 −4 ], p<0·0001, and RRR 0·30 [0·15–0·61], p=0·0008, respectively). Interpretation Several factors informed HTA outcomes for new cancer medicines. A high ESMO-MCBS score, defined as indicating substantial clinical benefit, increased the likelihood of a positive HTA outcome and shortened the interval between marketing authorisation and HTA outcome, and this association was not affected by other variables. Additional factors informing HTA outcomes include evidence uncertainties and unmet medical need. Country-relevant differences exist in the time-to-HTA outcome and the propensity of some countries to achieve positive (restricted or unrestricted) outcomes compared with others. Funding None.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".