Next Article in Journal
Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis
Next Article in Special Issue
Estimating Similarity of Dose–Response Relationships in Phase I Clinical Trials—Case Study in Bridging Data Package
Previous Article in Journal
A Study on the Causal Process of Virtual Reality Tourism and Its Attributes in Terms of Their Effects on Subjective Well-Being during COVID-19
Previous Article in Special Issue
Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models
Open AccessReview

Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges

by 1,2,* and 3
1
Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, USPC, Université de Paris, F-75006 Paris, France
2
F-CRIN PARTNERS Platform, AP-HP, Université de Paris, F-75010 Paris, France
3
Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Ivo M. Foppa
Int. J. Environ. Res. Public Health 2021, 18(3), 1022; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18031022
Received: 29 November 2020 / Revised: 15 January 2021 / Accepted: 20 January 2021 / Published: 24 January 2021
(This article belongs to the Special Issue Bayesian Design in Clinical Trials)
The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed. View Full-Text
Keywords: Bayesian; rare disease; prior distribution; meta-analysis; sample size Bayesian; rare disease; prior distribution; meta-analysis; sample size
MDPI and ACS Style

Ursino, M.; Stallard, N. Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges. Int. J. Environ. Res. Public Health 2021, 18, 1022. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18031022

AMA Style

Ursino M, Stallard N. Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges. International Journal of Environmental Research and Public Health. 2021; 18(3):1022. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18031022

Chicago/Turabian Style

Ursino, Moreno; Stallard, Nigel. 2021. "Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges" Int. J. Environ. Res. Public Health 18, no. 3: 1022. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18031022

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop