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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2605.03554 |
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| _version_ | 1866918484508672000 |
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| author | Asikanius, Elina Wolbers, Marcel Akacha, Mouna Brandt, Andreas Hofner, Benjamin Häring, Dieter A. Roes, Kit C. B. Vandemeulebroecke, Marc Wright, David Zinserling, Joerg Rufibach, Kaspar |
| author_facet | Asikanius, Elina Wolbers, Marcel Akacha, Mouna Brandt, Andreas Hofner, Benjamin Häring, Dieter A. Roes, Kit C. B. Vandemeulebroecke, Marc Wright, David Zinserling, Joerg Rufibach, Kaspar |
| contents | Over time, clinical trials have increasingly incorporated complex design and analysis elements such as interim analyses, adaptations, multiple endpoints, and sophisticated multiplicity schemes for multiple endpoints and/or treatment arms following the paradigm of frequentist inference. In frequentist clinical trials multiplicity can come from (at least) four sources: multiple looks at the data, multiple endpoints, multiple populations, or multiple treatment comparisons. Normally, Type 1 error control across the multiple hypotheses is implemented to control chance of false positive decisions. To achieve this advanced techniques such as adaptive designs or graphical multiple testing procedures have been developed and are used in the design of clinical trials. However, these methods focus on hypothesis testing while subsequent estimation remains crucial to allow for a benefit-risk assessment and further use of the results by various stakeholders. Through examples, we illustrate challenges in estimation and transparent communication. In general, there are no simple solutions to this conceptual and communicational challenge. The purpose of this paper is to generate awareness of these issues and initiate a discussion about how to address them moving forward. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03554 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Communicating results in trials with multiple hypotheses or adaptive design features Asikanius, Elina Wolbers, Marcel Akacha, Mouna Brandt, Andreas Hofner, Benjamin Häring, Dieter A. Roes, Kit C. B. Vandemeulebroecke, Marc Wright, David Zinserling, Joerg Rufibach, Kaspar Methodology Over time, clinical trials have increasingly incorporated complex design and analysis elements such as interim analyses, adaptations, multiple endpoints, and sophisticated multiplicity schemes for multiple endpoints and/or treatment arms following the paradigm of frequentist inference. In frequentist clinical trials multiplicity can come from (at least) four sources: multiple looks at the data, multiple endpoints, multiple populations, or multiple treatment comparisons. Normally, Type 1 error control across the multiple hypotheses is implemented to control chance of false positive decisions. To achieve this advanced techniques such as adaptive designs or graphical multiple testing procedures have been developed and are used in the design of clinical trials. However, these methods focus on hypothesis testing while subsequent estimation remains crucial to allow for a benefit-risk assessment and further use of the results by various stakeholders. Through examples, we illustrate challenges in estimation and transparent communication. In general, there are no simple solutions to this conceptual and communicational challenge. The purpose of this paper is to generate awareness of these issues and initiate a discussion about how to address them moving forward. |
| title | Communicating results in trials with multiple hypotheses or adaptive design features |
| topic | Methodology |
| url | https://arxiv.org/abs/2605.03554 |