Power comparison of robust approximate and non‐parametric tests for the analysis of cross‐over trials
Bibliographic record
Abstract
The main advantage of cross-over designs in practice is the use of a smaller number of subjects to produce treatment comparisons with sufficient precision. Bellavance and Tardif proposed a non-parametric approach to test the hypotheses of direct treatment and carry-over effects for the three-treatment three-period and six sequences cross-over design and showed the high asymptotic efficiency of their approach relative to the classical F-test based on ordinary least squares (OLS). In a more recent paper, Ohrvik suggested another non-parametric method for the analysis of cross-over trials. The power of these two non-parametric approaches is evaluated for small sample sizes via simulations, and compared to the power of the usual analysis of variance model based on OLS and a modified F-test approximation that take into account the correlation structure of the repeated measurements within subjects. Different covariance structures, sample sizes, and probability distributions for the responses, namely normal and gamma, are used in the simulations to evaluate the power and robustness of these different methods of analysis.
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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 | 0.157 | 0.514 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".