Non-Inferiority Trials: A Systematic Review on Methodological Quality and Reporting Standards
Bibliographic record
Abstract
BACKGROUND: Non-inferiority (NI) trials require unique trial design and methods, which pose challenges in their interpretation and applicability, risking introduction of inferior therapies in clinical practice. With the abundance of novel therapies, NI trials are increasing in publication. Prior studies found inadequate quality of reporting of NI studies, but were limited to certain specialties/journals, lacked NI margin evaluation, and did not examine temporal changes in quality. We conducted a systematic review without restriction to journal type, journal impact factor, disease state or intervention to evaluate the quality of NI trials, including a comprehensive risk of bias assessment and comparison of quality over time. METHODOLOGY: We searched PubMed and Cochrane Library databases for NI trials published in English in 2014 and 2019. They were assessed for: study design and NI margin characteristics, primary results, and risk of bias for blinding, concealment, analysis method and missing outcome data. RESULTS: We included 823 studies. Between 2014 and 2019, a shift from publication in specialty to general journals (15% vs 28%, p < 0.001) and from pharmacological to non-pharmacological interventions (25% vs 38%, p = 0.025) was observed. The NI margin was specified in most trials for both years (94% vs 95%). Rationale for the NI margin increased (36% vs 57%, p < 0.001), but remained low, with clinical judgement the most common rationale (30% vs 23%), but more 2019 articles incorporating patient values (0.3% vs 21%, p < 0.001). Over 50% of studies were open-label for both years. Gold standard method of analyses (both per protocol + (modified) intention to treat) declined over time (43% vs 36%, p < 0.001). DISCUSSION: The methodological quality and reporting of NI trials remains inadequate although improving in some areas. Improved methods for NI margin justification, blinding, and analysis method are warranted to facilitate clinical decision-making.
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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 | 0.465 | 0.734 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.020 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".