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Lookup NU author(s): Professor Kevin Wilson
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 The Author. Statistics in Medicine published by John Wiley & Sons Ltd.We consider the design of a two-arm superiority cluster randomized controlled trial (RCT) with a continuous outcome. We detail Bayesian inference for the analysis of the trial using a linear mixed-effects model. The treatment is compared to control using the posterior distribution for the treatment effect. We develop the form of the assurance to choose the sample size based on this analysis, and its evaluation using a two loop Monte Carlo sampling scheme. We assess the proposed approach, considering the effect of different forms of prior distribution, and the number of Monte Carlo samples needed in both loops for accurate determination of the assurance and sample size. Based on this assessment, we provide general advice on each of these choices. We apply the approach to the choice of sample size for a cluster RCT into poststroke incontinence, and compare the resulting sample size to that from assurance based on a Wald test for the treatment effect. The Bayesian approach to design and analysis developed in this article can offer advantages in terms of an increase in the robustness of the chosen sample size to parameter mis-specification and reduced sample sizes if prior information indicates the treatment effect is likely to be larger than the minimal clinically important difference.
Author(s): Wilson KJ
Publication type: Article
Publication status: Published
Journal: Statistics in Medicine
Year: 2023
Volume: 42
Issue: 25
Pages: 4517-4531
Print publication date: 10/11/2023
Online publication date: 20/08/2023
Acceptance date: 27/07/2023
Date deposited: 19/09/2023
ISSN (print): 0277-6715
ISSN (electronic): 1097-0258
Publisher: John Wiley and Sons Ltd
URL: https://doi.org/10.1002/sim.9871
DOI: 10.1002/sim.9871
Data Access Statement: R code used to implement the methods is available in the R package bayesiantrials found at https://newcastlerse.github.io/bayesiantrials/. Data sharing is not applicable to this article as no new data were created or analyzed in this study.
PubMed id: 37599065
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