Information borrowing in bayesian clinical trials: choice of tuning parameters for the robust mixture prior
The formal use of external data can increase the efficiency of clinical trial designs, enabling smaller sample sizes in settings where recruitment is challenging. In the Bayesian framework, such borrowing is achieved through informative priors, but substantial inconsistency (“drift”) between externa...
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| Auteurs principaux: | , , , , |
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| Format: | Article (Journal) |
| Langue: | anglais |
| Publié: |
05 May 2026
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| In: |
Statistics in biopharmaceutical research
Year: 2026, Pages: 1-14 |
| ISSN: | 1946-6315 |
| DOI: | 10.1080/19466315.2026.2646537 |
| Accès en ligne: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1080/19466315.2026.2646537
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| Notes sur l'auteur: | Vivienn Weru, Annette Kopp-Schneider, Manuel Wiesenfarth, Sebastian Weber, Silvia Calderazzo |
| Résumé: | The formal use of external data can increase the efficiency of clinical trial designs, enabling smaller sample sizes in settings where recruitment is challenging. In the Bayesian framework, such borrowing is achieved through informative priors, but substantial inconsistency (“drift”) between external and current trial data can compromise inference. Robust mixture priors address this risk by blending an informative prior with a diffuse component, allowing dynamic borrowing that adapts to the degree of drift. Yet guidance on choosing the mixture’s form, mean, variance, and weight remains limited. We study how these tuning parameters affect inference in one-arm and hybrid-control trials with normally distributed endpoints. Because the robust component is intended to protect against prior misspecification under drift - and these parameters determine the prior’s behavior across drift scenarios - we assess their impact on key operating characteristics. All four quantities substantially influence Type I error, power, and estimation accuracy. As expected, the robust component’s variance governs robustness, but its location has a strong and often underappreciated effect on testing and estimation error. Moreover, the mixture weight interacts closely with the robust component’s location and variance. We provide practical recommendations for selecting robust component parameters, mixture weights, alternative functional forms, and strategies for evaluating operating characteristics. |
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| Description: | Gesehen am 06.08.2026 |
| Description matérielle: | Online Resource |
| ISSN: | 1946-6315 |
| DOI: | 10.1080/19466315.2026.2646537 |