In common with many cluster-randomised trials, it was difficult to determine the appropriate sample size for the planned trial of the effectiveness of a systematic voiding programme for post-stroke incontinence due to the lack of a robust estimate of the intra-cluster correlation coefficient (ICC). One approach to overcome this problem is a method of combining ICC values in the Bayesian framework (Turner et al. 2005). We adopted this approach and used Bayesian hierarchical modelling to estimate the ICC

Leathley, Michael John

Sutton, Chris J

Thomas, Lois Helene

Tishkovskaya, Svetlana

Watkins, Caroline Leigh

Trials

English

PubMed

Svetlana Tishkovskaya

Chris Sutton

Lois Thomas

Michael Leathley

Caroline Watkins

Crossref

Determining the sample size for a cluster-randomised trial: Bayesian hierarchical modelling of the ICC estimate

In common with many cluster-randomised trials, it was difficult to determine the appropriate sample size for the planned trial of the effectiveness of a systematic voiding programme for post-stroke incontinence due to the lack of a robust estimate of the intra-cluster correlation coefficient (ICC). One approach to overcome this problem is a method of combining ICC values in the Bayesian framework (Turner et al. 2005). We adopted this approach and used Bayesian hierarchical modelling to estimate the ICC.To obtain the ICCs to combine in the model, we performed a literature search and identified a number of relevant studies with ICC estimates. To allow for uncertainty in ICC estimates, the Bayesian hierarchical model includes separate weights for each study and each outcome: study weights represent the degree of relevance to study population and intervention, and outcome weights are similarly related to the planned trial’s primary outcome (frequency of urinary incontinence). Assigning weights is rather subjective and is a source of uncertainty in the model. To minimise this subjectivity, we performed an exercise by which a team of expert reviewers with a range of relevant expertise assigned weights for each trial and each outcome. These weights were incorporated into the model to combine the individual ICC estimates and construct a distribution of a targeted ICC estimate to inform the sample size.This approach should provide a more robust estimate of an ICC for determining the sample size for a cluster-randomised trial as it combines relevant ICCs in Bayesian framework

S, Tishkovskaya

Sutton, Christopher

L, Thomas

M, Leathley

C, Watkins

The University of Manchester - Institutional Repository

Determining the sample size for a cluster-randomised trial: Bayesian hierarchical modelling of the ICC estimate.

CLoK

POSTER PRESENTATION Open AccessDetermining the sample size for a cluster-randomised trial: Bayesian hierarchical modellingof the ICC estimateSvetlana Tishkovskaya, Chris Sutton*, Lois Thomas, Michael Leathley, Caroline WatkinsFrom 3rd International Clinical Trials Methodology ConferenceGlasgow, UK. 16-17 November 2015In common with many cluster-randomised trials, it wasdifficult to determine the appropriate sample size forthe planned trial of the effectiveness of a systematicvoiding programme for post-stroke incontinence due tothe lack of a robust estimate of the intra-cluster correla-tion coefficient (ICC). One approach to overcome thisproblem is a method of combining ICC values in theBayesian framework (Turner et al. 2005). We adoptedthis approach and used Bayesian hierarchical modellingto estimate the ICC.To obtain the ICCs to combine in the model, we per-formed a literature search and identified a number ofrelevant studies with ICC estimates. To allow for uncer-tainty in ICC estimates, the Bayesian hierarchical modelincludes separate weights for each study and each out-come: study weights represent the degree of relevance tostudy population and intervention, and outcome weightsare similarly related to the planned trial’s primary out-come (frequency of urinary incontinence). Assigningweights is rather subjective and is a source of uncertaintyin the model. To minimise this subjectivity, we per-formed an exercise by which a team of expert reviewerswith a range of relevant expertise assigned weights foreach trial and each outcome. These weights were incor-porated into the model to combine the individual ICCestimates and construct a distribution of a targeted ICCestimate to inform the sample size.This approach should provide a more robust estimateof an ICC for determining the sample size for a cluster-randomised trial as it combines relevant ICCs in Bayesianframework.Published: 16 November 2015doi:10.1186/1745-6215-16-S2-P229Cite this article as: Tishkovskaya et al.: Determining the sample size for acluster-randomised trial: Bayesian hierarchical modelling of the ICCestimate. Trials 2015 16(Suppl 2):P229.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitUniversity of Central Lancashire, PrestonTishkovskaya et al. Trials 2015, 16(Suppl 2):P229http://www.trialsjournal.com/content/16/S2/P229 TRIALS© 2015 Tishkovskaya et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, providedthe original work is properly cited. 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.

http://dx.doi.org/10.1186/1745-6215-16-S2-P229

Determining the sample size for a cluster-randomised trial: Bayesian hierarchical modelling of the ICC estimate

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