Methods of Analysis in Randomized Noninferiority Trials: Methodological Survey Review Protocol
Bibliographic record
Abstract
Background: Noninferiority (NI) trial designs that investigate whether an experimental intervention is no worse than the standard of care have been used increasingly in recent years. The robustness of the conclusions depends in part on the analysis population set used. In NI settings, the intention-to-treat (ITT) and per-protocol (PP) analysis sets are most common. The ITT analysis has been considered anticonservative compared with the PP analysis. Objective: This study aimed to conduct a methodological review assessing the analysis population sets used in contemporary NI trials. Methods: A comprehensive electronic search strategy will be conducted to identify studies indexed in MEDLINE, Embase, Emcare, and Cochrane CENTRAL. Studies will be included if they are NI trials published in 2024. The primary outcome is the analysis population used for the primary analysis of the trial (ITT, PP, or as-treated). Secondary outcomes include the NI margin, effect estimates, point estimates, and corresponding CIs. Analyses will be performed using descriptive statistics. Results: The comprehensive search initially identified 1209 studies, of which 403 trials were eligible for data extraction. Data extraction began in January 2025 and is expected to be completed in January 2026. Conclusions: This methodological survey of NI trials will describe the analysis population used in the primary analysis and assess factors that may be associated with each analysis method.
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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 | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | 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 | 0.332 | 0.440 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.061 | 0.021 |
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".