Non‐inferiority trial design for recurrent events
Bibliographic record
Abstract
We describe methods for designing non-inferiority trials with recurrent event responses arising from mixed-Poisson models. Sample size formulae are derived for trials in which treatment effects are expressed as relative rates and as absolute differences in cumulative mean functions at a particular time. Simulation studies are conducted to provide empirical validation of the frequency properties of the design and testing procedures under the null and alternative hypotheses using both mixed-Poisson models and robust marginal methods. The robustness of the design to mis-specification of the random effect distribution is also studied empirically. Sample size requirements based on the proposed method are contrasted with those from a design based on the time to the first event for a motivating study of patients with bone metastases at risk of skeletal complications. When the between-patient heterogeneity in the event rate is small, there may be a considerable reduction in sample size with recurrent event outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.280 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".