A noninferiority design for a delayed calcineurin inhibitor substitution trial in kidney transplantation
Bibliographic record
Abstract
Improving long-term kidney transplant outcomes requires novel treatment strategies, including delayed calcineurin inhibitor (CNI) substitution, tested using informative trial designs. An alternative approach to the usual superiority-based trial is a noninferiority trial design that tests whether an investigational agent is not unacceptably worse than standard of care. An informative noninferiority design, with biopsy-proven acute rejection (BPAR) as the endpoint, requires determination of a prespecified, evidence-based noninferiority margin for BPAR. No such information is available for delayed CNI substitution in kidney transplantation. Herein we analyzed data from recent kidney transplant trials of CNI withdrawal and "real world" CNI- based standard of care, containing subjects with well-documented evidence of immune quiescence at 6 months posttransplant-ideal candidates for delayed CNI substitution. Our analysis indicates an evidence-based noninferiority margin of 13.8% for the United States Food and Drug Administration's composite definition of BPAR between 6 and 24 months posttransplant. Sample size estimation determined that ~225 randomized subjects would be required to evaluate noninferiority for this primary clinical efficacy endpoint, and superiority for a renal function safety endpoint. Our findings provide the basis for future delayed CNI substitution noninferiority trials, thereby increasing the likelihood they will provide clinically implementable results and achieve regulatory approval.
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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 | 0.266 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".