Interim analysis for binary outcome trials with a long fixed follow-up time and repeated outcome assessments at pre-specified times
Bibliographic record
Abstract
In trials with binary outcomes, assessed repeatedly at pre-specified times and where the subject is considered to have experienced a failure at the first occurrence of the outcome, interim analyses are performed, generally, after half or more of the subjects have completed follow-up. Depending on the duration of accrual relative to the length of follow-up, this may be inefficient, since there is a possibility that the trial will have completed accrual prior to the interim analysis. An alternative is to plan the interim analysis after subjects have completed follow-up to a time that is less than the fixed full follow-up duration. Using simulations, we evaluated three methods to estimate the event proportion for the interim analysis in terms of type I and II errors and the probability of early stopping. We considered: 1) estimation of the event proportion based on subjects who have been followed for a pre-specified time (less than the full follow-up duration) or who experienced the outcome; 2) estimation of the event proportion based on data from all subjects that have been randomized by the time of the interim analysis; and 3) the Kaplan-Meier approach to estimate the event proportion at the time of the interim analysis. Our results show that all methods preserve and have comparable type I and II errors in certain scenarios. In these cases, we recommend using the Kaplan-Meier method because it incorporates all the available data and has greater probability of early stopping when the treatment effect exists.
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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 | 0.181 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".