Application of Group Sequential Methods to the 2-in-1 Design and Its Extensions for Interim Monitoring
Why this work is in the frame
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Bibliographic record
Abstract
The 2-in-1 adaptive design (Chen et al. 2018) allows seamless expansion of an ongoing Phase 2 trial into a Phase 3 trial to expedite a drug development program. Under a mild assumption expected to generally hold in practice, as Slepian’s lemma guarantees, the trial can be tested at the full alpha level with or without expansion, sacrificing no statistical power. The endpoint used for expansion decisions can be the same as or different from the primary endpoints, and there is no restriction on the expansion threshold. Due to its flexibility and robustness, it has drawn immediate attention from academic researchers and industry practitioners. The design has been substantially extended in the literature and successfully implemented in multiple trials.Group sequential methods are a cornerstone in trial monitoring. A preliminary investigation (Chen, Li, and Deng) suggests that it can be naturally incorporated into the 2-in-1 design without providing formal mathematical proof. In this article, we fill the gap by providing a sufficient condition that is expected to generally hold in practice to unlock the full potential of the 2-in-1 design and pave the way for its broader applications. In practice, the condition can be verified with trial data as needed using simulation studies per the FDA guideline on adaptive designs. We also discuss a special case that guarantees the validity without the need for any simulation checking.
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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 | 0.033 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it