Time‐to‐event clinical trial designs: Existing evidence and remaining concerns
Bibliographic record
Abstract
Well-designed placebo-controlled clinical trials are critical to the development of novel treatments for epilepsy, but their design has not changed for decades. Patients, clinicians, regulators, and innovators all have concerns that recruiting for trials is challenging, in part, due to the static design of maintaining participants for long periods on add-on placebo when there are an increasing number of options for therapy. A traditional trial maintains participants on blinded treatment for a static period (e.g., 12 weeks of maintenance), during which participants on placebo have an elevated risk of sudden unexpected death in epilepsy compared to patients on an active treatment. Time-to-event trials observe participants on blinded treatment until a key event occurs (e.g., post-randomization seizure count matches pre-randomization monthly seizure count). In this article, we review the evidence for these designs based on re-analysis of prior trials, one published trial that used a time-to-second seizure design, and experience from an ongoing blinded trial. We also discuss remaining concerns regarding time-to-event trials. We conclude that, despite potential limitations, time-to-event trials are a potential promising mechanism to make trials more patient friendly and reduce placebo exposure, which are urgent needs to improve safety and increase recruitment to trials.
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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 | 0.287 | 0.447 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".