Carry‐over in cross‐over trials in bioequivalence: theoretical concerns and empirical evidence
Bibliographic record
Abstract
Abstract There is now general agreement that pre‐testing for carry‐over in the AB/BA design is harmful and that efficient analysis of this design must proceed on the assumption that carry‐over has not affected the results to any appreciable degree. A general consensus has not been achieved in the case of higher‐order designs. Since particular forms of carry‐over can be estimated on a within‐patient basis and unbiased within‐patient treatment estimators are possible, some statisticians favour pre‐testing and some favour automatic adjustment for carry‐over. We present theoretical arguments that show that, just as in the AB/BA case, the strategy of pre‐testing is biased as a whole and also that the loss in terms of efficiency in adjusting is not negligible. We also present data from two large series of bioequivalence studies to provide empirical evidence that in this context carry‐over is either absent or rare. We conclude that adjusting or testing for carry‐over in bioequivalence studies is at worst harmful and at best pointless, and that this may also apply to other kinds of study. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.392 | 0.611 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| 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".