Testing for non-inferior mortality: a systematic review of non-inferiority margin sizes and trial characteristics
Bibliographic record
Abstract
OBJECTIVE: To describe the size and variability of non-inferiority margins used in non-inferiority trials of medications with primary outcomes involving mortality, and to examine the association between trial characteristics and non-inferiority margin size. DESIGN: Systematic review. DATA SOURCES: Medline, Medline In Process, Medline Epub Ahead of Print and Embase Classic+Embase databases from January 1989 to December 2019. ELIGIBILITY CRITERIA: Prospective non-inferiority randomised controlled trials comparing pharmacological therapies, with primary analyses for non-inferiority and primary outcomes involving mortality alone or as part of a composite outcome. Trials had to prespecify non-inferiority margins as absolute risk differences or relative to risks of outcome and provide a baseline risk of primary outcome in the control intervention. RESULTS: 3992 records were screened, 195 articles were selected for full text review and 111 articles were included for analyses. 82% of trials were conducted in thrombosis, infectious diseases or oncology. Mortality was the sole primary outcome in 23 (21%) trials, and part of a composite primary outcome in 88 (79%) trials. The overall median non-inferiority margin was an absolute risk difference of 9% (IQR 4.2%-10%). When non-inferiority margins were expressed relative to the baseline risk of primary outcome in control groups, the median relative non-inferiority margin was 1.5 (IQR 1.3-1.7). In multivariable regression analyses examining the association between trial characteristics (medical specialty, inclusion of paediatric patients, mortality as a sole or part of a composite primary outcome, presence of industry funding) and non-inferiority margin size, only medical specialty was significantly associated with non-inferiority margin size. CONCLUSION: Absolute and relative non-inferiority margins used in published trials comparing medications are large, allowing conclusions of non-inferiority in the context of large differences in mortality. Accepting the potential for large increases in outcomes involving mortality while declaring non-inferiority is a challenging methodological issue in the conduct of non-inferiority trials.
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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 | 0.164 | 0.452 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".