Point estimation for adaptive trial designs. II: Practical considerations and guidance
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Publication:6625791
DOI10.1002/sim.9734zbMATH Open1545.62521MaRDI QIDQ6625791
Laura Flight, Thomas Jaki, Munya Dimairo, Babak Choodari-Oskooei, Philip Pallmann, David S. Robertson
Publication date: 28 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Drop-the-losers design: binomial case
- A general framework for the analysis of adaptive experiments
- Group Sequential and Confirmatory Adaptive Designs in Clinical Trials
- Simultaneous confidence intervals that are compatible with closed testing in adaptive designs
- A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection
- Adaptive Group Sequential Designs for Clinical Trials: Combining the Advantages of Adaptive and of Classical Group Sequential Approaches
- A Two-Stage Design for Choosing among Several Experimental Treatments and a Control in Clinical Trials
- Randomization in Clinical Trials
- Conditional estimation following a group sequential clinical trial
- On the bias of maximum likelihood estimation following a sequential test
- Supplementary Analysis at the Conclusion of a Sequential Clinical Trial
- Drop-the-Losers Design: Normal Case
- Sample Size Recalculation in Internal Pilot Study Designs: A Review
- Confirmatory Seamless Phase II/III Clinical Trials with Hypotheses Selection at Interim: General Concepts
- Confirmatory Seamless Phase II/III Clinical Trials with Hypotheses Selection at Interim: Applications and Practical Considerations
- Sample size re‐estimation in clinical trials
- Bias induced by adaptive dose-finding designs
- Randomised trials with provision for early stopping for benefit (or harm): the impact on the estimated treatment effect
- Point estimation for adaptive trial designs. I: A methodological review
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Point estimation, confidence intervals, and \(P\)-values for optimal adaptive two-stage designs with normal endpoints ⋮ A class of computational methods to reduce selection bias when designing phase 3 clinical trials
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