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Lookup NU author(s): Samuel Sarkodie, Professor James WasonORCiD, Dr Michael Grayling
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.This study presents a hybrid (Bayesian-frequentist) approach to sample size re-estimation (SSRE) for cluster randomised trials with continuous outcome data, allowing for uncertainty in the intra-cluster correlation (ICC). In the hybrid framework, pre-trial knowledge about the ICC is captured by placing a Truncated Normal prior on it, which is then updated at an interim analysis using the study data, and used in expected power control. On average, both the hybrid and frequentist approaches mitigate against the implications of misspecifying the ICC at the trial's design stage. In addition, both frameworks lead to SSRE designs with approximate control of the type I error-rate at the desired level. It is clearly demonstrated how the hybrid approach is able to reduce the high variability in the re-estimated sample size observed within the frequentist framework, based on the informativeness of the prior. However, misspecification of a highly informative prior can cause significant power loss. In conclusion, a hybrid approach could offer advantages to cluster randomised trials using SSRE. Specifically, when there is available data or expert opinion to help guide the choice of prior for the ICC, the hybrid approach can reduce the variance of the re-estimated required sample size compared to a frequentist approach. As SSRE is unlikely to be employed when there is substantial amounts of such data available (ie, when a constructed prior is highly informative), the greatest utility of a hybrid approach to SSRE likely lies when there is low-quality evidence available to guide the choice of prior.
Author(s): Sarkodie SK, Wason JMS, Grayling MJ
Publication type: Article
Publication status: Published
Journal: Statistics in Medicine
Year: 2024
Pages: epub ahead of print
Online publication date: 28/08/2024
Acceptance date: 07/08/2024
Date deposited: 09/09/2024
ISSN (print): 0277-6715
ISSN (electronic): 1097-0258
Publisher: John Wiley and Sons Ltd
URL: https://doi.org/10.1002/sim.10205
DOI: 10.1002/sim.10205
Data Access Statement: The data that support the findings of this study are openly available in Github at https://github.com/sks2023/article_codes.
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