Inadequate Statistical Power of Negative Clinical Trials in Urological Literature
Bibliographic record
Abstract
PURPOSE: Negative studies provide valuable information. However, conducting studies with inadequate power is unethical and an inefficient use of resources. The purpose of this study was to determine the prevalence of negative studies with inadequate power in urological literature. MATERIALS AND METHODS: The Journal of Urology, Urology and BJU International (formerly British Journal of Urology) from 1982 to 2002 were searched using the Ovid MEDLINE database. All clinical trials that contained the phrase "no difference" were identified. Data necessary for power calculation were extracted from applicable studies. RESULTS: Of the 417 articles identified in the MEDLINE search, 127 were negative studies that contained enough information to be analyzed. There were 70 (55%) articles from The Journal of Urology, 35 (28%) from BJU International and 22 (17%) from Urology. Of the studies that used continuous variables 65% had adequate power (greater than 80%) to detect a 50% difference between groups and 32% had adequate power to detect a 25% difference. Of the studies that used dichotomous variables only 33% had adequate power to detect a 50% difference between groups and 23% had adequate power to detect a 25% difference. Levels of adequate power in negative studies did not improve over time (p = 0.258). CONCLUSIONS: Many negative studies in urological literature are inconclusive because they lack adequate power to detect even large differences between groups. Inadequately powered studies often result in false conclusions that alter clinical behavior and deter further research. Therefore, it is imperative to consider power when interpreting literature. When designing future investigations power calculations should be performed to ensure sufficient patient recruitment to attain clinically meaningful results.
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 | 0.631 | 0.863 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".