Ensuring Superior Reporting of Radiation Therapy Noninferiority Trials: A Systematic Review
Bibliographic record
Abstract
Purpose: Although the frequency of noninferiority trials is increasing, the consistency of the reporting of these trials can vary. The aim of this systematic review was to assess the reporting quality of radiation therapy noninferiority trials. Methods and Materials: The PubMed, Embase, and Cochrane databases were queried for randomized controlled radiation therapy trials with noninferiority hypotheses published in English between January 2000 and July 2022, and this was performed by an information scientist. Descriptive statistics were used to summarize data. Results: Of 423 records screened, 59 (14%) were included after full-text review. All were published after 2003 and open label. The most common primary cancer type was breast (n = 15, 25%). Altered radiation fractionation (n = 26, 45%) and radiation de-escalation (n = 11, 19%) were the most common types of interventions. The most common primary endpoints were locoregional control (n = 17, 29%) and progression-free survival (n = 14, 24%). Fifty-three (90%) reported the noninferiority margin, and only 9 (17%) provided statistical justification for the margin. The median absolute noninferiority margin was 9% (interquartile range, 5%-10%), and the median relative margin was 1.51 (interquartile range, 1.33-2.04). Sample size calculations and confidence intervals were reported in 54 studies (92%). Both intention-to-treat and per-protocol analyses were reported in 27 studies (46%). In 31 trials (53%), noninferiority of the primary endpoint was reached. Conclusions: There was variability in the reporting of key components of noninferiority trials. We encourage consideration of additional statistical reasoning such as guidelines or previous trials in the selection of the noninferiority margin, reporting both absolute and relative margins, and the avoidance of statistically vague or misleading language in the reporting of future noninferiority 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 | 0.562 | 0.853 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".