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An Application of the Multivariate Linear Mixed Model to the Analysis of Shoulder Complexity in Breast Cancer Patients

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Listed:
  • Gholamreza Oskrochi

    (Department of Mechanical Engineering an Mathematical Sciences, Oxford Brookes University, Wheatley Campus, Wheatley, Oxford OX33 1HX, UK)

  • Emmanuel Lesaffre

    (Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), Kapucijnenvoer 35 blok D, B-3000 Leuven, Belgium)

  • Youssof Oskrochi

    (Department of Primary Care and Public Health, Imperial College London, Charing Cross Hospital, London W6 8RP, UK)

  • Delva Shamley

    (Clinical Research Centre, University of Cape Town, Old Main Building, L51. Groote Schuur Hospital Observatory, Cape Town 7700, South Africa)

Abstract

In this study, four major muscles acting on the scapula were investigated in patients who had been treated in the last six years for unilateral carcinoma of the breast. Muscle activity was assessed by electromyography during abduction and adduction of the affected and unaffected arms. The main principal aim of the study was to compare shoulder muscle activity in the affected and unaffected shoulder during elevation of the arm. A multivariate linear mixed model was introduced and applied to address the principal aims. The result of fitting this model to the data shows a huge improvement as compared to the alternatives.

Suggested Citation

  • Gholamreza Oskrochi & Emmanuel Lesaffre & Youssof Oskrochi & Delva Shamley, 2016. "An Application of the Multivariate Linear Mixed Model to the Analysis of Shoulder Complexity in Breast Cancer Patients," IJERPH, MDPI, vol. 13(3), pages 1-13, March.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:3:p:274-:d:64900
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    References listed on IDEAS

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    1. Brent A. Coull & Alan Agresti, 2000. "Random Effects Modeling of Multiple Binomial Responses Using the Multivariate Binomial Logit-Normal Distribution," Biometrics, The International Biometric Society, vol. 56(1), pages 73-80, March.
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