Kidwell KM, Roychoudhury S, Wendelberger B, Scott J, Moroz T, Yin S, Majumder M, Zhong J, Huml RA, Miller V. Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles.
Orphanet J Rare Dis 2022;
17:186. [PMID:
35526036 PMCID:
PMC9077995 DOI:
10.1186/s13023-022-02342-5]
[Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background
Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.
Main text
Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.
Conclusion
The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13023-022-02342-5.
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