Disparity between statistical significance and clinical importance in published randomised controlled trials: a methodological study
Bibliographic record
Abstract
OBJECTIVES: We estimated the extent of the disparity between statistical significance and clinical importance in published randomised controlled trials (RCTs), and explored factors associated with this disparity. DESIGN: A methodological study of trials published between 2018 and 2022 and indexed in PubMed was conducted. Primary reports of two-arm, phase three, superiority trials of human health interventions were included. Pharmacokinetic studies and pilot trials were excluded. The relationship between the specified delta value or minimum clinically important difference (as specified in the sample size calculation) and the effect size determined the clinical importance of the trial results. Studies where the clinical importance was at least possible, with no statistical significance, were classified as SS-CI+ disparity, and studies which were definitely not clinically important but statistically significant were classified as SS+CI- disparity. Factors associated with each type of disparity were explored at the study level using multinomial logistic regression. RESULTS: 500 trials were included. In 38.4% (n=192) of these, information was not available to classify clinical importance. Overall disparity was found in 63 of the remaining 308 studies, 20.5% (95% confidence interval (CI) 16.2% to 25.5%). SS+CI- disparity was 10.3% (15/145) (95% CI 6.1% to 16.8%) and SS-CI+ disparity was 29.5% (48/163) (95% CI 22.7% to 37.2%).Studies testing complementary or alternative medicines relative to drug trials were positively associated with SS+CI- disparity. Low journal impact factor, small sample size, unfunded or grant funding and failure to mention allocation concealment were positively associated with SS-CI+disparity. CONCLUSIONS: In up to 20% of RCTs, there may be a disparity between statistical significance and clinical importance. Clinical importance of results should be taken into account in the interpretation of trial results, and trials should adhere stringently to reporting guidelines.
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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 | 0.707 | 0.879 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".