Two‐stage design of clinical trials involving recurrent events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mixed Poisson models are often used for the design of clinical trials involving recurrent events since they provide measures of treatment effect based on rate and mean functions and accommodate between individual heterogeneity in event rates. Planning studies based on these models can be challenging when there is a little information available on the population event rates, or the extent of heterogeneity characterized by the variance of individual-specific random effects. We consider methods for adaptive two-stage clinical trial design, which enable investigators to revise sample size estimates using data collected during the first phase of the study. We describe blinded procedures in which the group membership and treatment received by each individual are not revealed at the interim analysis stage, and a 'partially blinded' procedure in which group membership is revealed but not the treatment received by the groups. An EM algorithm is proposed for the interim analyses in both cases, and the performance is investigated through simulation. The work is motivated by the design of a study involving patients with immune thrombocytopenic purpura where the aim is to reduce bleeding episodes and an illustrative application is given using data from a cardiovascular trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.088 | 0.783 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it