Statistical Power of Negative Randomized Controlled Trials Presented at American Society for Clinical Oncology Annual Meetings
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To investigate the prevalence of underpowered randomized controlled trials (RCTs) presented at American Society of Clinical Oncology (ASCO) annual meetings. METHODS: We surveyed all two-arm phase III RCTs presented at ASCO annual meetings from 1995 to 2003 for which negative results were obtained. Post hoc calculations were performed using a power of 80% and an alpha level of .05 (two sided) to determine sample sizes required to detect small, medium, and large effect sizes. For studies reporting a proportion or time-to-event as primary end point, effect size was expressed as an odds ratio (OR) or hazard ratio (HR), respectively, with a small effect size defined as OR/HR >or= 1.3, medium effect size defined as OR/HR >or= 1.5, and large effect size defined as OR/HR >or= 2.0. Logistic regression was used to identify factors associated with lack of statistical power. RESULTS: Of 423 negative RCTs for which post hoc sample size calculations could be performed, 45 (10.6%), 138 (32.6%), and 233 (55.1%) had adequate sample size to detect small, medium, and large effect sizes, respectively. Only 35 negative RCTs (7.1%) reported a reason for inadequate sample size. In a multivariable model, studies that were presented at oral sessions (P = .0038), multicenter studies supported by a cooperative group (P < .0001), and studies with time to event as primary outcome (P < .0001) were more likely to have adequate sample size. CONCLUSION: More than half of negative RCTs presented at ASCO annual meetings do not have an adequate sample to detect a medium-size treatment effect.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.196 | 0.602 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.022 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it