Evaluation of Performance of Adaptive Designs Based on Treatment Effect Intervals
Bibliographic record
Abstract
The accuracy of the treatment effect estimation is crucial to the success of Phase 3 studies. The calculation of sample size relies on the treatment effect estimation and cannot be changed during the trial in a fixed sample size design. Oftentimes, with limited efficacy data available from early phase studies and relevant historical studies, the sample size calculation may not accurately reflect the true treatment effect. Several adaptive designs have been proposed to address this uncertainty in the sample size calculation. These adaptive designs provide flexibility of sample size adjustment during the trial by allowing early trial stopping or sample size adjustment at interim look(s). The use of adaptive designs can optimize the trial performance when the treatment effect is an assumed constant value. However in practice, it may be more reasonable to consider the treatment effect within an interval rather than as a point estimate. Because proper selection of adaptive designs may decrease the failure rate of Phase 3 clinical trials and increase the chance for new drug approval, this paper proposes measures and evaluates the performance of different adaptive designs based on treatment effect intervals, and identifies factors that may affect the performance of adaptive designs.
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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 | 0.119 | 0.346 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".