Required efficacy for novel therapies in BCG‐unresponsive non‐muscle invasive bladder cancer: Do current recommendations really reflect clinically meaningful outcomes?
Bibliographic record
Abstract
BACKGROUND: Single-arm trials are currently an accepted study design to investigate the efficacy of novel therapies (NT) in non-muscle invasive bladder cancer (NMIBC) unresponsive to intravesical Bacillus Calmette-Guérin (BCG) immunotherapy as randomized controlled trials are either unfeasible (comparator: early radical cystectomy; ERC), or unethical (comparator: placebo). To guide the design of such single-arm trials, expert groups published recommendations for clinically meaningful outcomes. The aim of this study was to quantitatively verify the appropriateness of these recommendations. METHODS: We used a discrete event simulation framework in combination with a supercomputer to find the required efficacy at which a NT can compete with ERC when it comes to quality-adjusted life expectancy (QALE). In total, 24 different efficacy thresholds (including the recommendations) were investigated. RESULTS: After ascertaining face validity with content experts, repeated verification, external validation, and calibration we considered our model valid. Both recommendations rarely showed an incremental benefit of the NT over ERC. In the most optimistic scenario, an increase in the IBCG recommendation by 10% and an increase in the FDA/AUA recommendation by 5% would yield results at which a NT could compete with ERC from a QALE perspective. CONCLUSIONS: This simulation study demonstrated that the current recommendations regarding clinically meaningful outcomes for single-arm trials evaluating the efficacy of NT in BCG-unresponsive NMIBC may be too low. Based on our quantitative approach, we propose increasing these thresholds to at least 45%-55% at 6 months and 35% at 18-24 months (complete response rates/recurrence-free survival) to promote the development of clinically truly meaningful NT.
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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 | 0.110 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".