Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/32588
Title: Changing cluster composition in cluster randomised controlled trials: design and analysis considerations
Authors: Corrigan, Neil
Bankart, Michael J. G.
Gray, Laura J.
Smith, Karen L.
First Published: 24-May-2014
Publisher: BioMed Central
Citation: Trials, 2014, 15 : 184
Abstract: BACKGROUND: There are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes. METHODS: We explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis. RESULTS: Cluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated. CONCLUSIONS: Examples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations include avoidance of cluster merges where possible, discontinuation of clusters following heterogeneous merges, allowance for potential loss of clusters and additional variability in cluster size in the original sample size calculation, and use of appropriate ICC estimates that reflect cluster size.
DOI Link: 10.1186/1745-6215-15-184
eISSN: 1745-6215
Links: http://www.trialsjournal.com/content/15/1/184
http://hdl.handle.net/2381/32588
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2014 Corrigan et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.
Appears in Collections:Published Articles, Dept. of Health Sciences

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