Disparity between statistical and clinical significance in published randomised controlled trials indexed in PubMed: a protocol for a cross-sectional methodological survey
Bibliographic record
Abstract
INTRODUCTION: The commonly used frequentist paradigm of null hypothesis statistics testing with its reliance on the p-value and the corresponding notion of 'statistical significance' has been under ongoing criticism. Misinterpretation and misuse of the p-value have contributed to publication bias, unreliable studies, frequent false positives, fraud and mistrust in results of scientific studies. While p-values themselves are still useful, part of the problem may be the confusion between statistical and clinical significance. In randomised controlled trials of health interventions, this confusion could lead to erroneous conclusions about treatment efficacy, research waste and compromised patient outcomes. The extent to which clinical and statistical significance of published randomised clinical trials do not match is not known. This is a protocol for a methodological study to understand the extent of the problem of disparities between statistical and clinical significance in published clinical trials, and to identify and assess the factors associated with discrepant results in these studies. METHODS AND ANALYSIS: A methodological survey of published randomised controlled trials is planned. Trials published between 2018 and 2022 and their protocols will be searched and screened for inclusion, with a planned sample size of 500 studies. The reported minimum clinically important difference, the study effect size and confidence intervals will be used to assess clinical importance of trial results. Comparison of statistical significance and clinical importance of the trial results will be used to determine disparity. Data will be analysed to estimate the outcomes, and factors associated with disparate study results will be assessed using logistic regression analysis. ETHICS AND DISSEMINATION: Ethical approval for the study has been granted by Stellenbosch University's Health Research Ethics Committee. This is part of a larger study towards a PhD in Biostatistics and will be disseminated as a thesis, conference abstract and peer-reviewed manuscript.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.352 | 0.462 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.018 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".