Approach to uncertainty in health technology assessment in a Central and Eastern European country: appraisal of cancer drugs by a Polish HTA agency in presence of high crossover rates in clinical trials
Bibliographic record
Abstract
Background: Health technology assessment (HTA) plays an important role in reimbursement decision making in Poland and its principles are similar to those used in other countries. However, specific inter-country differences, such as substantial divergence in budgetary resources, may lead to variation in actual HTA practices, e.g. in the approach to uncertainty. Cancer drug reimbursement is a decision-taking area associated with substantial uncertainty. One of its important sources is the presence of crossover (treatment switching) in clinical trials. Objectives: To review the appraisal processes completed for cancer drugs by the Polish HTA agency (AOTMiT) and to compare AOTMiT to the British, Australian and Canadian HTA bodies with respect to strategies of addressing crossover-related uncertainty. Methods: Cancer drug assessment processes in AOTMiT, where a substantial crossover took place were reviewed and subsequently matched with the assessments conducted by NICE, PBAC and pCODR. Ways to approach the crossover-related uncertainty, the influence of uncertainty on the recommendation and uncertainty management strategies were examined. Results: 29 HTA processes related to 6 drugs were included. The crossover rate ranged from 51% to 85% and ITT analyses did not show statistically significant survival benefit. AOTMiT more often yielded negative recommendation, showed less consistent approach to crossover-related uncertainty and a narrower scope of adopted uncertainty management strategies. Conclusions: Crossover constitutes a vital source of uncertainty in the assessments of new cancer therapies. The lack of consistent standards decreases the transparency of assessment processes and can contribute to undertaking suboptimal reimbursement 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 | 0.356 | 0.432 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".