Non-Price-Related Determinants of Value and Access for Novel Non-small Cell Lung Cancer Treatments: A Cross-Country Review of HTA Decision Making
Bibliographic record
Abstract
INTRODUCTION: Access and funding for newly approved treatments for non-small cell lung cancer (NSCLC) are often dependent on Health Technology Assessment (HTA) involving cost-effectiveness analysis. Whilst methods used by HTA agencies share many similarities, final decisions may differ. This may be the result, not just of price considerations, but also of variation in value judgements by different agencies. The aim of this study was to review international HTA evaluations to identify determinants of value and access for NSCLC treatments. METHODS: A targeted review and analysis was undertaken of published HTAs for NSCLC across HTA agencies in six countries (Australia, Canada, England, France, Ireland and Scotland). Analysis of extracted data consisted of three stages: descriptive analysis, bivariate analysis and multivariable analysis. RESULTS: The analysis included 163 HTAs that assessed oncological treatments for NSCLC from 2003 to 2019. The majority of HTA decisions (67.5%) were positive. However, some evidence of heterogeneity in HTA decisions and the factors informing them were identified. The most influential factors included in the multivariate model related to the HTA agency conducting the appraisal, the year of market authorisation, treatment type and the line of treatment. CONCLUSION: Heterogenous decision-making frameworks can present a challenge to developing HTA submissions. This research contributes to understanding decision-making factors and why countries make different decisions.
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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 | 0.108 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".