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Sample size considerations for trials using cerebral white matter hyperintensity progression as an intermediate outcome at 1 year after mild stroke: results from a prospective cohort study

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    Rights statement: © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Original languageEnglish
Early online date21 Feb 2017
Publication statusE-pub ahead of print - 21 Feb 2017


Background White matter hyperintensities (WMH) are commonly seen on in brain imaging, and are associated with stroke and cognitive decline. Therefore, they may provide a relevant intermediate outcome in clinical trials. WMH can be measured as a volume or visually on the Fazekas scale. We investigated predictors of WMH progression and design of efficient studies using WMH volumes and Fazekas score as an intermediate outcome. Methods We prospectively recruited 264 patients with mild ischaemic stroke and measured WMH volumes, Fazekas score, age, and cardiovascular risk factors at baseline and 1 year. We modelled predictors of WMH burden at 1 year and used the results in sample size calculations for hypothetical randomised controlled trials with different analysis plans and length of follow-up. Results Follow-up WMH volume was predicted by baseline WMH - a 0.73ml (95%CI 0.65 to 0.80, p<0.0001) increase per 1ml baseline volume increment, and 2.93ml increase (95%CI 1.76 to 4.10, p<0.0001) per point on the Fazekas scale. Using a mean difference of 1ml in WMH volume between treatment groups, 80% power, and 5% alpha, adjusting for all predictors and 2 year follow-up produced the smallest sample size (642). Other study designs produced samples sizes from 2054 to 21270. Sample size calculations using Fazekas scores as outcome using the same power and alpha, and an odds ratio corresponding to a 1ml difference were sensitive to assumptions, and ranged from 2504 to 18886. Conclusions Baseline WMH volume and Fazekas scores predict follow-up WMH volume. Study size was smallest using volumes and longer term follow-up, but this must be balanced against resources required to measure volumes versus Fazekas scores, bias due to drop-out and scanner drift. Samples sizes based on Fazekas scores may be best estimated with simulation studies.

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