Oncology drugs for orphan indications: how are HTA processes evolving for this specific drug category?
Bibliographic record
Abstract
Orphan drugs (ODs) are intended for the diagnosis, prevention, or treatment of rare diseases. Many cancer subtypes, including all childhood cancers, are defined as rare diseases, and over one-third of ODs are now intended to treat oncology indications. However, market access for oncology ODs is becoming increasingly challenging; ODs are prone to significant uncertainty around their cost-effectiveness, while payers must balance the need for these vital innovations with growing sensitivity to rising costs. The objective of this review was to evaluate different mechanisms that have been introduced to facilitate patient access to oncology ODs in five different countries (Australia, Canada, England, France, and Sweden), using eight oncology ODs and non-orphan oncology drugs as examples of their application. A targeted literature review of health technology assessment (HTA) agency websites was undertaken to identify country-specific guidance and HTA documentation for recently evaluated oncology ODs and non-orphan oncology drugs. None of these countries were found to have explicit HTA criteria for the assessment of ODs, and therefore, oncology ODs are assessed through the usual HTA process. However, distinct and additional processes are adopted to facilitate access to oncology ODs. Review of eight case-study drugs showed that these additional assessment processes were rarely used, and decisions were largely driven by proving cost-effectiveness using standard incremental cost-effectiveness ratio (ICER) thresholds. The predominant implication arising from this study is that application of standard HTA criteria to oncology ODs in many countries fails to take into account any uncertainties around their clinical- and cost-effectiveness, resulting in disparities in HTA reimbursement decisions based on differences in addressing or accepting uncertainty. In order to address this issue, HTA agencies should adopt a more flexible approach to cost-effectiveness, as typified by the Tandvårds-och Läkemedelsförmånsverket in Sweden, which takes into account the small patient numbers involved, limited budget impact, and high unmet medical needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.130 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".