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ADDIS-Graphs for Online Error Control With Application to Platform Trials
Lasse Fischer1, Marta Bofill Roig2, Werner Brannath1
1Competence Center for Clinical Trials Bremen, University of Bremen, Bremen, Germany.
Biometrical Journal. Biometrische Zeitschrift
|September 28, 2025
Summary
We introduce ADDIS-Graphs, a flexible new method for online error control in studies like platform trials. This approach improves statistical power and adaptivity for testing multiple hypotheses efficiently.
Area of Science:
- Statistical methodology
- Biostatistics
- Clinical trial design
Background:
- Online error control is crucial for sequential hypothesis testing, managing familywise error rate (FWER) or false discovery rate (FDR).
- Existing methods are often rigid, designed for large-scale studies, and lack flexibility for smaller, adaptive trials like platform trials.
- Platform trials face unique challenges including dependent p-values due to shared control arms and the need for prespecified significance levels.
Purpose of the Study:
- To propose a novel, flexible, and interpretable graphical method for online error control in sequential hypothesis testing.
- To address the limitations of existing methods in smaller studies and platform trial settings.
- To enhance statistical power and adaptivity in hypothesis testing while maintaining strict error control.
Main Methods:
- Introduction of adaptive-discarding-Graphs (ADDIS-Graphs) for familywise error rate (FWER) control.
- Development of extensions to ADDIS-Graphs, incorporating information on the joint distribution of p-values.
- Creation of a version of ADDIS-Graphs for false discovery rate (FDR) control.
Main Results:
- ADDIS-Graphs demonstrate provable uniform improvement over state-of-the-art methods.
- The graphical structure of ADDIS-Graphs allows for perfect adaptation to platform trial settings.
- Extensions enhance the method's ability to leverage information from dependent p-values and control FDR.
Conclusions:
- ADDIS-Graphs offer a powerful and flexible solution for online error control in sequential hypothesis testing.
- The proposed methods are particularly well-suited for adaptive platform trials and similar settings.
- These advancements provide improved statistical efficiency and interpretability in complex study designs.
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