Pragmatic monitoring of emerging efficacy data in randomized controlled trials
Bibliographic record
Abstract
Monitoring the conduct of phase III randomized controlled trials is driven by ethical reasons to protect the study integrity and the safety of trial participants. We propose a group sequential, pragmatic approach for monitoring the accumulating efficacy information in randomized controlled trials. The "Population Health Research Institute boundary" is simple to implement and sensible, as it considers the reduction in uncertainty with increasing information as the study progresses. It is also pragmatic, since it takes into consideration the typical monitoring behavior of monitoring committees of large multicenter trials and is relatively easily implemented. It not only controls the overall Lan-DeMets type I error probability (alpha) spent, but performs better than other group sequential boundaries for the total nominal study alpha. We illustrate the use of our monitoring approach in the early termination of two past completed trials.
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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 | 0.743 | 0.993 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.036 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".