Bayesian sample size determination using commensurate priors to leverage preexperimental data.
Publication Date
2022-03-06Journal Title
Biometrics
ISSN
0006-341X
Publisher
Wiley
Language
en
Type
Article
This Version
AO
VoR
Metadata
Show full item recordCitation
Zheng, H., Jaki, T., & Wason, J. M. (2022). Bayesian sample size determination using commensurate priors to leverage preexperimental data.. Biometrics https://doi.org/10.1111/biom.13649
Abstract
This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant preexperimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions, or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on preexperimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pretrial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with an elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.
Keywords
BIOMETRIC METHODOLOGY, Bayesian experimental designs, historical data, rare‐disease trials, robustness, sample size
Sponsorship
NIHR Academy (SRF-2015-08-001)
Medical Research Council (MC_UU_00002/14)
Identifiers
biom13649
External DOI: https://doi.org/10.1111/biom.13649
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335607
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk