Challenges of clinical trial design when there is lack of clinical equipoise: use of a response-conditional crossover design
Bibliographic record
Abstract
Clinical equipoise is widely accepted as the basis of ethics in clinical research and requires investigators to be uncertain of the relative therapeutic merits of trial comparators. When clinical equipoise is in question, innovative trial designs are needed to reduce ethical tension while satisfying regulators' requirements. We report a novel response-conditional crossover study design used in a Phase 3, randomized, double-blind, placebo-controlled clinical trial of intravenous 10% caprylate-chromatography purified immunoglobulin for chronic inflammatory demyelinating polyradiculoneuropathy. During the initial 24-week period, patients crossed over to the alternative treatment at the first sign of deterioration or if they failed to improve or were unable to maintain improvement at any time after 6 weeks. This trial design addressed concerns about lack of equipoise raised by physicians interested in trial participation and proved acceptable to regulatory authorities. The trial design may be applicable to other studies where clinical equipoise is in question.
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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 | 0.713 | 0.656 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".