Robertson, David S. and Burnett, Thomas and Choodari-Oskooei, Babak and Dimairo, Munya and Grayling, Michael and Pallmann, Philip and Jaki, Thomas (2025) Confidence Intervals for Adaptive Trial Designs II: Case Study and Practical Guidance. STATISTICS IN MEDICINE, 44 (18-19): e70202. ISSN 0277-6715, 1097-0258
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In adaptive clinical trials, the conventional confidence interval (CI) for a treatment effect is prone to undesirable properties such as undercoverage and potential inconsistency with the final hypothesis testing decision. Accordingly, as is stated in recent regulatory guidance on adaptive designs, there is the need for caution in the interpretation of CIs constructed during and after an adaptive clinical trial. However, it may be unclear which of the available CIs in the literature are preferable. This paper is the second in a two-part series that explores CIs for adaptive trials. Part I provided a methodological review of approaches to construct CIs for adaptive designs. In this paper (Part II), we present an extended case study based around a two-stage group sequential trial, including a comprehensive simulation study of the proposed CIs for this setting. This facilitates an expanded description of considerations around what makes for an effective CI procedure following an adaptive trial. We show that the CIs can have notably different properties. Finally, we propose a set of guidelines for researchers around the choice of CIs and the reporting of CIs following an adaptive design.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | RANDOMIZATION; POINT; adaptive design; bootstrap; conditional inference; coverage; estimation; group sequential; interim analyses |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Computational Statistics (Prof. Dr. Thomas Jaki) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 11 Aug 2026 07:48 |
| Last Modified: | 11 Aug 2026 07:48 |
| URI: | https://pred.uni-regensburg.de/id/eprint/67117 |
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