The fragility of statistically significant findings from randomised controlled trials in the urological literature
Bibliographic record
Abstract
OBJECTIVES: To use the Fragility Index to evaluate the robustness of statistically significant findings from urological randomised controlled trials (RCTs). MATERIALS AND METHODS: The 'Fragility Index' is defined as the minimum number of patients in one arm of a trial whose status would have to change from 'event' to 'non-event', such that a statistically significant result becomes non-significant. We identified all RCTs published in four major urology journals between 2011 and 2015, and we determined the Fragility Index values for those trials reporting statistically significant results of dichotomous outcomes using the Fisher's exact test. RESULTS: In all, 332 RCTs were identified, and 41 studies met the inclusion criteria. The median (interquartile range) Fragility Index was 3 (1, 4.5), indicating that an addition of only three alternate events to one arm of a typical trial would have eliminated its statistical significance. In 27/40 cases (67.5% of cases), the number of patients lost to follow-up was larger than its Fragility Index. CONCLUSIONS: The results of urology RCTs that study dichotomous outcomes and report statistically significant differences between groups are sometimes fragile and depend on few events. Urologists should interpret these RCTs cautiously, particularly when the number of participants lost to follow-up exceeds the Fragility Index. Routine reporting of Fragility Index values alongside P values may provide additional guidance about the robustness of statistically significant findings.
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 | 0.670 | 0.910 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.030 | 0.024 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| 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".