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Lookup NU author(s): Professor Kevin WilsonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Background/Aims: Sample size determination for cluster randomised trials (CRTs) is challenging because it requires robust estimation of the intra-cluster correlation coefficient (ICC). Typically, the sample size is chosen to provide a certain level of power to reject the null hypothesis in a two sample hypothesis test. This relies on the minimal clinically important difference (MCID) and estimates for the overall standard deviation, the ICC and, if cluster sizes are assumed to be unequal, the coefficient of variation of the cluster size. Varying any of these parameters can have a strong effect on the required sample size. \track{In particular, it is very sensitive to small differences in the ICC. A relevant ICC estimate is often not available, or the available estimate is imprecise due to being based on studies with low numbers of clusters.} If the ICC value used in the power calculation is far from the unknown true value, this could lead to trials which are substantially over- or under-powered. Methods: In this paper, we propose a hybrid approach using Bayesian assurance to determine the sample size for a CRT in combination with a frequentist analysis. Assurance is an alternative to traditional power, which incorporates the uncertainty on key parameters through a prior distribution. We suggest specifying prior distributions for the overall standard deviation, ICC and coefficient of variation of the cluster size, while still utilising the MCID. We illustrate the approach through the design of a CRT in post-stroke incontinence and compare the results to those obtained from a standard power calculation. % The impacts of varying the ICC prior distribution are also considered.Results: We show that assurance can be used to calculate a sample size based on an elicited prior distribution for the ICC, whereas a power calculation discards all of the information in the prior except for a single point estimate. Results show that this approach \kw{can avoid misspecifying sample sizes when the prior medians for the ICC are very similar, but the underlying prior distributions exhibit quite different behaviour.} Incorporating uncertainty on all three of the nuisance parameters, rather than only on the ICC, does not notably increase the required sample size.Conclusion: Assurance provides a better understanding of the probability of success of a trial given a particular MCID and can be used instead of power to produce sample sizes which are more robust to parameter uncertainty. This is especially useful when there is difficulty obtaining reliable parameter estimates.
Author(s): Williamson SF, Tishkovskaya SV, Wilson KJ
Publication type: Article
Publication status: Published
Journal: Clinical Trials
Year: 2025
Pages: Epub ahead of print
Online publication date: 11/02/2025
Acceptance date: 27/11/2024
Date deposited: 28/11/2024
ISSN (print): 1740-7745
ISSN (electronic): 1740-7753
Publisher: Sage Publications Ltd
URL: https://doi.org/10.1177/17407745241312635
DOI: 10.1177/17407745241312635
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