Flexibility over rigor: stakeholder acceptance of the limitations of confirmatory studies following accelerated approval
Bibliographic record
Abstract
Introduction: Concerns about completing postmarketing requirements (PMRs) following accelerated approval (AA) of new drugs have been well documented. However, there has been little examination of specific barriers and facilitators to timely, rigorous PMRs (eg, blinded, randomized trials in the approved population) from the perspective of key stakeholders. Methods: To understand these factors, especially for cancer and rare diseases, we interviewed 56 regulators, industry executives, patient advocates, and payers. Results: Stakeholders focused on predictable PMR barriers and, except for payers, offered weak solutions, including those that would trade rigor for feasibility (eg, avoiding randomization, conducting PMRs outside approved indications), could raise other concerns (eg, conducting PMRs abroad), or are likely to fall short (eg, patient education). Stakeholders supported requiring that confirmatory studies begin before AA but were unsure how to retain rigor thereafter, emphasized tradeoffs, and sought rare disease exceptions. Although regulators and payers supported payment reforms for AA drugs, all stakeholder groups questioned practicability. Conclusion: Stakeholders recognize PMR shortcomings but prioritize flexibility, raising questions about AA's foundations and suggesting that further documenting poor rigor is unlikely to change policy. Beyond recent reforms, future efforts should emphasize confirming benefit for rare disease AAs, encouraging PMR rigor, and exploring AA payment reform.
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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 | 0.679 | 0.739 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".