An efficient alternative to the stratified Cox model analysis
Bibliographic record
Abstract
Consider a typical two-treatment randomized clinical trial involving a time-to-event endpoint, with randomization stratified by a categorical prognostic factor (for example gender). At the design stage, it is often assumed that the treatment hazard ratio (HR) is constant across the strata, and the data are commonly analyzed using the stratified Cox proportional hazards model. We caution that this ubiquitous approach is needlessly risky because departures from the assumption of the HR being the same for all the strata can result in a notably biased and/or less powerful analysis. An alternative approach is proposed in which first the [log] HR is estimated separately for each stratum using an unstratified Cox model, and then the stratum-specific estimates are combined for overall inference using either sample size or 'minimum risk' stratum weights. The advantages of the proposed two-step analysis versus the common one-step stratified Cox model analysis are illustrated using simulations that were conducted to support the design of a vaccine clinical trial.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".