Bibliographic record
Abstract
Virtually all clinical trials collect multiple endpoints that are usually correlated. Many methods have been proposed to control the family-wise type I error rate (FWER), but these methods often disregard the correlation among the endpoints, such as the commonly used Bonferroni correction, Holm procedure, Wiens' Bonferroni fixed-sequence (BFS) procedure and its extension, and the alpha-exhaustive fallback (AEF). Huque and Alosh proposed a flexible fixed-sequence (FFS) testing method, which extended the BFS method by taking into account correlations among endpoints. However, the FFS method faces a computational difficulty when there are four or more endpoints. Similar to the BFS procedure, the FFS method requires the prespecified testing sequence and the type I error rate used for first endpoint in the sequence (usually the most important endpoint) cannot be adjusted for the correlation among the endpoints or from the rejection of other null hypotheses for other endpoints. Thus, the power for this test is not maximized. In this paper, I present a weighted multiple testing correction for correlated tests. By using the package 'mvtnorm' in R, the proposed method can handle up to a thousand endpoints. Simulations show that the proposed method shares the advantage of the FFS and the AEF methods (having high power for the second or later hypotheses in the testing sequence) and has higher power for testing the first hypothesis than the FFS and the AEF methods. The proposed method has higher power for each individual hypothesis than the weighted Holm procedure, especially when the correlation between endpoints is high.
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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 | 0.129 | 0.447 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".