OP544 Appraising Variation In Health Technology Assessment Of Novel Immuno-Oncology Medicines In Australia, Canada, France, And The United Kingdom
Bibliographic record
Abstract
Introduction Demonstrating the value of medicines through health technology assessment (HTA) systems is becoming increasingly complex. Innovative therapies – such as immuno-oncology (IO) agents – are testing limits of methodological approaches in markets with established HTA systems. The objective of this study is to understand how requirements, approaches, and decision-making differ between select HTA agencies with a focus on specific PD-1/PD-L1 (programmed death receptor-1/programmed death-ligand 1) agents and cancer indications, and to describe how this variation impacts patient access. To achieve this objective, we conducted a detailed HTA dossier review for several recently launched IO products across Australia (AU), Canada (CA), France (FR), and the United Kingdom (UK). Methods Content experts reviewed HTA dossiers for pembrolizumab, nivolumab, and atezolizumab for non-small cell lung cancer (NSCLC) first-line monotherapy, NSCLC combination therapy, and adjuvant melanoma. A systematic analytic framework was developed to understand best-practice methodology across systems. Information on submitted data, patient/expert input, and access decisions were extracted; key themes were identified and refined through workshop discussion, and probed further through blinded primary research with eight individuals with current or recent experience of HTA systems. Results We identified six major elements of variation impacting decision-making: evidentiary expectations for biomarkers, use/impact of patient-centered data; use/impact of real-world data, acceptance of surrogate endpoints, approaches for clinical data extrapolation, and accepted time horizons. Considerable variation in time to access was observed; for pembrolizumab (NSCLC first-line monotherapy), time from product registration to HTA decision ranged from 42 (CA) to 487 (AU) days; time from registration to listing ranged from 189 (CA) to 605 (AU) days. Conclusions Evaluated HTA systems demonstrate a large degree of variability in approaches to decision-making for novel IO medicines; resultant access decisions and time to access are also highly variable. Inconsistency between systems and duplication of effort when assessing similar clinical/economic data could be contributing to limited or delayed patient access; the relationship merits further exploration. Assessed HTA systems are currently undergoing process revisions but expert input suggests that this is not expected to reduce variation, and could further increase complexity. The influence of parallel scientific advice programs between HTA agencies and regulatory bodies in reducing variation must also be determined.
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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 | 0.311 | 0.420 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".