Increasing the power of randomized trials comparing different treatment durations
Bibliographic record
Abstract
When the optimal treatment duration is uncertain, a randomized trial may allocate patients to receive active treatment for different durations. We use an example where patients receive treatment for 0, 24, or 52 weeks. In this trial, patients in the 24-weeks and 52-weeks arms receive the same treatment during the first 24 weeks. This overlap allows for more powerful analyses than conventional pair-wise comparisons of arms. When the outcome is the time-to-event, the power for the 0-weeks versus 24-weeks comparison can be increased by including patients in the 52-weeks arm as patients in the 24-weeks arm for the first 24 weeks and censoring at 24 weeks. Furthermore, differences observed between the 24-weeks and 52-weeks arms during the first 24 weeks can only reflect noise. Hence, the comparison of these two arms should be restricted to only patients who remain on the study at 24 weeks and include only the events after 24 weeks. Through simulation, we show that modified analyses accounting for these considerations increase study power substantially. Moreover, if patients were allocated equally to the arms, then events or discontinuations during the first 24 weeks will reduce the number of patients available for the 24-weeks versus 52-weeks comparison, and hence the power of this analysis will be lower than that for the 0-weeks versus 24-weeks comparison. We present a sample size calculation procedure for equalizing the power of these two analyses. Typically, this allocation requires much larger sample sizes in the 24-weeks and 52-weeks arms than in the 0-week arm.
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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 | 0.462 | 0.717 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier 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".