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Multivariate Analysis of Longitudinal Ordinal Data With Mixed Effects Models, With Application to Clinical Outcomes in Osteoarthritis

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  • Celine Marielle Laffont
  • Marc Vandemeulebroecke
  • Didier Concordet

Abstract

Our objective was to evaluate the efficacy of robenacoxib in osteoarthritic dogs using four ordinal responses measured repeatedly over time. We propose a multivariate probit mixed effects model to describe the joint evolution of endpoints and to evidence the intrinsic correlations between responses that are not due to treatment effect. Maximum likelihood computation is intractable within reasonable time frames. We therefore use a pairwise modeling approach in combination with a stochastic EM algorithm. Multidimensional ordinal responses with longitudinal measurements are a common feature in clinical trials. However, the standard methods for data analysis use unidimensional models, resulting in a loss of information. Our methodology provides substantially greater insight than these methods for the evaluation of treatment effects and shows a good performance at low computational cost. We thus believe that it could be used in routine practice to optimize the evaluation of treatment efficacy.

Suggested Citation

  • Celine Marielle Laffont & Marc Vandemeulebroecke & Didier Concordet, 2014. "Multivariate Analysis of Longitudinal Ordinal Data With Mixed Effects Models, With Application to Clinical Outcomes in Osteoarthritis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 955-966, September.
    • Handle: RePEc:taf:jnlasa:v:109:y:2014:i:507:p:955-966
    DOI: 10.1080/01621459.2014.917977
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    References listed on IDEAS

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    1. Yu-Zhu Tian & Man-Lai Tang & Wai-Sum Chan & Mao-Zai Tian, 2021. "Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings," Computational Statistics, Springer, vol. 36(2), pages 1289-1319, June.
    2. Dipankar Bandyopadhyay & Antonio Canale, 2016. "Non-parametric spatial models for clustered ordered periodontal data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 619-640, August.
    3. Chu, Amanda M.Y. & Omori, Yasuhiro & So, Hing-yu & So, Mike K.P., 2023. "A Multivariate Randomized Response Model for Sensitive Binary Data," Econometrics and Statistics, Elsevier, vol. 27(C), pages 16-35.

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