Comparison of Post Hoc Multiple Pairwise Testing Procedures as Applied to Small k-Group Logrank Tests
Bibliographic record
Abstract
The logrank test is widely used to compare groups on distribution of survival time in the presence of censoring. There is no convention for post hoc pairwise comparisons after a significant omnibus k-group logrank test. This simulation study compares four post hoc pairwise testing procedures: Bonferroni, Dunn-Šidák, Hochberg, and unadjusted post hoc logrank test procedure. Evaluation criteria include, familywise type I error rate, correct decision rate, number of correctly rejected pairs, and false discovery rate. We demonstrated that when conditioned upon rejection of the omnibus test, multiplicity adjustments may be unnecessary and can be overly conservative when k is at most 4, or number of comparisons is no greater than 6. This is supported by the results that the performance of the unadjusted post hoc logrank test procedure is preferred over the others on all criteria except for the false discovery rate. The Hochberg procedure appears to be superior among the adjustments examined. Data from a clinical trial for suicide prevention illustrate these approaches where number of comparison groups is often limited.
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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 | 0.142 | 0.458 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".