How to Use a Noninferiority Trial
Bibliographic record
Abstract
Clinical investigators are increasingly testing treatments that have the primary benefit of decreased burden or harms relative to an existing standard. The goal of the resulting randomized trials--called noninferiority trials--is to establish that the novel treatment's effectiveness is not substantially less than the existing standard. Conclusions from these trials are, however, based on noninferiority thresholds specified by authors whose judgments may not coincide with those of patients and clinicians. This article highlights issues related to validity, interpretation, and applicability of results specific to noninferiority trials. Suboptimal administration of standard treatment or exclusive reliance on the analyze-as-randomized approach that is standard for conventional superiority trials may produce misleading results in noninferiority trials. Clinicians should judge whether the novel treatment's impact on effectiveness outcomes--the prime reason for wanting to prescribe it--is sufficiently close to that of standard treatment that they are comfortable substituting it for the existing standard. Trading off desirable and undesirable consequences is an individual decision: given the benefits of a novel treatment, some patients may perceive the uncertainty regarding a reduction in treatment effectiveness as acceptable while others may not.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.483 | 0.761 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".