Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints
Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint’s variance. A fundamental problem of this approach is that the true distribution parameters are not known before the tr...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
19 January 2024
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| In: |
BMC medical research methodology
Year: 2024, Jahrgang: 24, Pages: 1-14 |
| ISSN: | 1471-2288 |
| DOI: | 10.1186/s12874-024-02150-4 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12874-024-02150-4 Verlag, kostenfrei, Volltext: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-024-02150-4 
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| Verfasserangaben: | Björn Bokelmann, Geraldine Rauch, Jan Meis, Meinhard Kieser and Carolin Herrmann |
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| 520 | |a Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint’s variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated. | ||
| 650 | 4 | |a Adaptive designs | |
| 650 | 4 | |a Binary endpoint | |
| 650 | 4 | |a Performance score | |
| 650 | 4 | |a Sample size recalculation | |
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