Surgical clinical trials with non-inferiority design: a cross-sectional bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Wide-spread concerns have been raised about possible bias in published surgical non-inferiority trials. Therefore, we performed a comprehensive bibliometric analysis to identify the existence of bias, and provided recommendations for future non-inferiority trials. METHODS: Databases including MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were systematically searched (last update on 27 April 2020) to include published phase II and phase III non-inferiority surgical trials. We collected general information and parameters associated with trial design. The association between extracted factors and establishment of non-inferiority was then analyzed. RESULTS: A total of 347 trials were included in this study. Only 13 (3.7%) trials reported the pre-specified non-inferiority margin in registration, and 99 (28.5%) trials justified margin selection in ultimate trial publications. A significant association was found between industry funding and increased odds of achieving non-inferiority [odds ratio (OR): 1.17, 95% confidence interval (CI): 1.06 to 1.30, P=0.001]. Moreover, trials which had been presented in conferences were less likely to claim non-inferiority (OR: 0.83, 95% CI: 0.69 to 0.99, P=0.035). CONCLUSIONS: Our study was the first quantitative analysis revealing the presence of biases in findings of existing surgical non-inferiority trials, which could possibly mislead surgeons' clinical decision making. We suggest improving reporting of detailed study design especially funding sources as well as margin justification for future trials. We also encourage conference presentation of ongoing trials prior to the ultimate publication.
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How this classification was reachedexpand
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 | 0.052 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.052 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads 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".