Fragility of randomized trials supporting cancer drug approvals stratified by approval pathway and review designations
Bibliographic record
Abstract
BACKGROUND: It has been suggested that the results from fragile trials are less likely to translate into benefit in routine clinical practice. METHODS: We searched the Food and Drug Administration (FDA) archives to identify drug approvals for solid organ malignancies between 2010 and 2019. We calculated the Fragility Index (FI) supporting each approval, using methods to account for time-to-event. We compared FI and trial and approval characteristics using Mann-Whitney U and Kruskal-Wallis test. Using logistic regression, we examined study characteristics associated with withdrawal of consent or lost to follow-up (WCLFU) exceeding the calculated FI. RESULTS: The median FI among 125 included studies was 23 (range 1-322). The FI was ≤10 in 35 studies (28%), 11-20 in 21 (17%), and >20 in 69 (55%). The median FI/Nexp was 7.7% (range 0.1-51.7%). The median FI was significantly lower among approvals processed through the accelerated vs regular pathway (5.5 vs 25, p = 0.001), but there was no difference in median FI/Nexp. The WCLFU exceeded FI in 42% of studies. Overall survival endpoints were more likely to have a WCLFU exceeding FI (OR 3.16, p = 0.003). WCLFU exceeding FI was also associated with a lesser magnitude of effect (median HR 0.69 vs 0.55, p < 0.001). In a sensitivity analysis including only studies with 1:1 randomization, 51% of studies had WCLFU >FI. CONCLUSION: The median FI among all trials was 23, and WCLFU exceeded FI in 42%. Comparative trials in solid tumors supporting approval through the accelerated pathway are more fragile compared to trials approved through the regular pathway, an observation likely explained by a lower sample size in the experimental arm.
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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 | 0.425 | 0.729 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".