Noninferiority Margin Size and Acceptance of Trial Results: Contingent Valuation Survey of Clinician Preferences for Noninferior Mortality
Bibliographic record
Abstract
ObjectivesWe used modified contingent valuation methodology to determine how noninferiority margin sizes influence clinicians’ willingness to accept clinical trial results that compare mortality in critically ill children.MethodsWe surveyed pediatric infectious diseases and critical care clinicians in Canada, Australia, and New Zealand and randomized respondents to review 1 of 9 mock abstracts describing a noninferiority trial of bacteremic critically ill children assigned to 7 or 14 d of antibiotics. Each scenario showed higher mortality in the 7-d group but met noninferiority criterion. We explored how noninferiority margins and baseline mortality rates influenced respondent acceptance of results.ResultsThere were 106 survey respondents: 65 (61%) critical care clinicians, 28 (26%) infectious diseases physicians, and 13 (12%) pharmacists. When noninferiority margins were 5% and 10%, 73% (24/33) and 79% (27/33) respondents would accept shorter treatment, compared with 44% (17/39) when the margin was 20% (P = 0.003). Logistic regression adjusted for baseline mortality showed 5% and 10% noninferiority margins were more likely to be associated with acceptance of shorter treatment compared with 20% margins (odds ratio [OR] 3.5, 95% confidence interval [CI]: 1.3–9.6, P = 0.013; OR 5.1, 95% CI: 1.8–14.6, P = 0.002). Baseline mortality was not a significant predictor of acceptance of shorter treatment.ConclusionsClinicians are more likely to accept shorter treatment when noninferiority margins are ≤10%. However, nearly half of respondents who reviewed abstracts with 20% margins were still willing to accept shorter treatment. This is a novel application of contingent valuation methodology to elicit acceptance of research results among end users of the medical literature.HighlightsClinicians are more likely to accept shorter treatment durations based on noninferior mortality results when the noninferiority margin is 5% or 10% than if the margin is 20%.However, nearly half of clinicians would still accept shorter-duration treatment as noninferior with margins of 20%.Baseline mortality does not independently predict acceptance of shorter-duration treatment.Contingent valuation is a novel approach to elicit the acceptance of research design parameters from the perspective of endusers of the medical literature.
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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 | 0.080 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".