• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Sample size calculations for cluster randomised trials, with a focus on ordinal outcomes. 
    •   QMRO Home
    • Queen Mary University of London Theses
    • Theses
    • Sample size calculations for cluster randomised trials, with a focus on ordinal outcomes.
    •   QMRO Home
    • Queen Mary University of London Theses
    • Theses
    • Sample size calculations for cluster randomised trials, with a focus on ordinal outcomes.
    ‌
    ‌

    Browse

    All of QMROCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    ‌
    ‌

    Administrators only

    Login
    ‌
    ‌

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Sample size calculations for cluster randomised trials, with a focus on ordinal outcomes.

    View/Open
    Robinson_Clare_PhD_Final_160516.pdf (4.543Mb)
    Publisher
    Queen Mary University of London
    Metadata
    Show full item record
    Abstract
    Background A common approach to sample size calculation for cluster randomised trials (CRTs) is to calculate the sample size assuming individual randomisation and multiply it by an inflation factor, the design effect. This approach is well established for binary and continuous outcomes, but less so for ordinal. As the variety in trial design increases alternative or more complex methods are required. There is currently no single resource that provides a comprehensive summary of methods. This thesis aims to provide a unique contribution towards the review and development of sample size methods for CRTs, with a focus on ordinal outcomes. Methods I provide a comprehensive review of sample size methods for CRTs and summarise the methodological gaps that remain. Through simulation I evaluate the power performance, under realistic trial scenarios, of the design effect for ordinal outcomes calculated using a kappa-type intracluster correlation coefficient (ICC), the ICC on an assumed underlying variable and an ANOVA ICC. I provide practical guidance for sample size calculation for ordinal outcomes in CRTs. Results Simulation results showed when the number of clusters was large the ANOVA and kappa-type estimates were equivalent, and smaller than the latent variable ICC. Use of the ANOVA ICC in the design effect produced adequately powered trials and power was marginally reduced under a minor deviation from the common assumption of proportional odds used in ordered regression. Conclusions For outcomes with three to five categories the ANOVA ICC, calculated by assigning numerical equally spaced scores to the ordinal categories, can be used in the simple design effect to produce an adequately powered trial. The method assumes an analysis by random effects ordered regression with proportional odds, a reasonable number of clusters, and clusters of the same size.
    Authors
    Robinson, Clare Marie
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/12913
    Collections
    • Theses [3600]
    Copyright statements
    The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author
    Twitter iconFollow QMUL on Twitter
    Twitter iconFollow QM Research
    Online on twitter
    Facebook iconLike us on Facebook
    • Site Map
    • Privacy and cookies
    • Disclaimer
    • Accessibility
    • Contacts
    • Intranet
    • Current students

    Modern Slavery Statement

    Queen Mary University of London
    Mile End Road
    London E1 4NS
    Tel: +44 (0)20 7882 5555

    © Queen Mary University of London.