Statistical analysis plan for Love Your Brain: a multi-arm randomised controlled trial of a stroke prevention digital platform
Kilkenny, M.F., Gall, S.L., Cadilhac, D.A., Thrift, A.G., Nelson, M.R., Bray, J., Cameron, J., Kleinig, T., Murphy, L., Purvis, T., Freak-Poli, R., Burns, C., Farmer, C., Bullas, B., Dalli, L.L., Horton, E., Booth, B., Ho, S., and Olaiya, M.T. (2026) Statistical analysis plan for Love Your Brain: a multi-arm randomised controlled trial of a stroke prevention digital platform. Trials, 27 (1). 259.
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Abstract
Background: Stroke is common, affecting an estimated one in four people in their lifetime. Fortunately, stroke is also highly preventable. With the rise in digital literacy and the increasing adoption of digital health tools, a promising avenue for stroke prevention is through digitally-delivered interventions. Love Your Brain: A stroke prevention digital platform is a three-year research project to develop and evaluate a digital platform to 1) increase participant visits to their medical practitioner for assessment or management of cardiovascular risk factors (primary outcome); and 2) improve participants’ health-related stroke knowledge, adherence to prevention medications and uptake of healthy or risk-modifying behaviour (secondary outcomes). The project is a multi-arm randomised controlled trial, comparing the common control arm to each of the intervention arms (either an online course or text messages). We present the statistical analysis plan for this trial. Methods/design: Participants are randomised 1:1:1 in variable block sizes, with stratification balancing by age and sex. The sample size of 894 participants was calculated to detect a 30% relative intervention effect, with 80% power and 5% significance level (two-sided). Recruitment will end when the sample size is achieved (adjusting for ≤ 10% attrition rate). The primary outcome will be compared separately for each intervention arm with the common control arm. As the outcome is binary, log-binomial regression models will be used with adjustment for stratification variables (i.e., gender, age groups [45–64 and >= 65 years]) and covariates that demonstrate imbalances between arms at baseline. Secondary outcomes will be evaluated using generalised mixed effects regression models (including linear, log-binomial, or quantile regression). The primary outcome analysis will be based on intention-to-treat. A p-value ≤ 0.05 will indicate statistical significance. Conclusions: This statistical analysis plan ensures transparency in reporting the trial outcomes. Love Your Brain will provide novel evidence on the effectiveness of two digital health education platforms for the prevention of stroke. Trial registration: ACTRN12625000124437; U1111-1305-2964 Feasibility Pilot: Australian New Zealand Clinical Trials Registry: ACTRN12624000540516; Universal Trial Number: U1111-1305-2964. SAP Version: 1.0 (September 2025) Protocol version: 1.0 (December, 2024) SAP revisions: Nil
| Item ID: | 94315 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 1745-6215 |
| Keywords: | Digital health, EHealth, Randomized controlled trial, Statistical analysis plan, Stroke |
| Copyright Information: | © The Author(s) 2026. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Date Deposited: | 16 Oct 2026 05:26 |
| FoR Codes: | 42 HEALTH SCIENCES > 4206 Public health > 420605 Preventative health care @ 100% |
| SEO Codes: | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280112 Expanding knowledge in the health sciences @ 100% |
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