Design of platform trials with a change in the control treatment arm
Bibliographic record
Abstract
Platform trials are an efficient way of testing multiple treatments. We consider platform trials where, if a treatment is found to be superior to the control, it will become the new standard of care. The remaining treatments are then tested against this new control. In this setting, one can either keep the information on both the new standard of care and the other active treatments before the control is changed or discard this information when testing for benefit of the remaining treatments. We show analytically and numerically, retaining the information collected before the change in control can be detrimental to the power in a frequentist multi-arm multi-stage trial. Specifically, we consider the overall power, the probability that the active treatment with the greatest treatment effect is found during the trial, and the conditional power, the probability a given treatment is found superior against the current control. Also studied is the conditional type I error, the probability a given treatment is incorrectly found superior against the current control. We prove when retaining the information decreases both the overall and conditional power but also decreases the conditional type I error. A motivating example is then studied. Based on these observations, we discuss different aspects to consider when deciding whether to run a continuous platform trial or run an inherently new trial using the same trial infrastructure.
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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 | 0.013 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".