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Lookup NU author(s): Lou Whitehead, Professor James WasonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the efficacy of a new treatment in clinical research. Relevant information from previous studies may be desirable to incorporate in the design and analysis of a new trial, with the Bayesian paradigm providing a coherent framework to formally incorporate prior knowledge. Many established methods involve the use of a discounting factor, sometimes related to a measure of ‘similarity’ between historical and the new trials. However, it is often the case that the sample size is highly nonlinear in those discounting factors. This hinders communication with subject-matter experts to elicit sensible values for borrowing strength at the trial design stage. Focussing on a method that can incorporate historical data from multiple sources, we highlight a particular issue of nonmonotonicity and explain why this causes issues with interpretability of discounting factors (hereafter referred to as ‘weights’). We propose a solution from which an analytical sample size formula is derived. We then propose a linearization technique such that the sample size changes uniformly over the weights. This leads to interpretable weights (as a percentage of information to borrow/discount) which could facilitate easier elicitation of expert opinion on their values.
Author(s): Whitehead LE, Wason JMS, Sailer O, Zheng H
Publication type: Article
Publication status: Published
Journal: Statistical Methods in Medical Research
Year: 2026
Pages: Epub ahead of print
Online publication date: 06/04/2026
Acceptance date: 02/04/2018
Date deposited: 06/05/2026
ISSN (print): 0962-2802
ISSN (electronic): 1477-0334
Publisher: Sage Publications Ltd.
URL: https://doi.org/10.1177/0962280226143
DOI: 10.1177/09622802261432816
Data Access Statement: R code for reproducing the Motivating Example and Performance Evaluation is posted online at GitHub: https://github.com/lou-e-whitehead/BayesianSSD_2024.
PubMed id: 41989351
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