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Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method

Author

Listed:
  • Hao Bai

    (Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
    These authors contributed equally to this work.)

  • Yuan Zhong

    (Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
    These authors contributed equally to this work.)

  • Xin Gao

    (Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

  • Wei Xu

    (Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A1, Canada)

Abstract

In clinical research, study outcomes usually consist of various patients’ information corresponding to the treatment. To have a better understanding of the effects of different treatments, one often needs to analyze multiple clinical outcomes simultaneously, while the data are usually mixed with both continuous and discrete variables. We propose the multivariate mixed response model to implement statistical inference based on the conditional grouped continuous model through a pairwise composite-likelihood approach. It can simplify the multivariate model by dealing with three types of bivariate models and incorporating the asymptotical properties of the composite likelihood via the Godambe information. We demonstrate the validity and the statistic power of the multivariate mixed response model through simulation studies and clinical applications. This composite-likelihood method is advantageous for statistical inference on correlated multivariate mixed outcomes.

Suggested Citation

  • Hao Bai & Yuan Zhong & Xin Gao & Wei Xu, 2020. "Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method," Stats, MDPI, vol. 3(3), pages 1-18, July.
    • Handle: RePEc:gam:jstats:v:3:y:2020:i:3:p:16-220:d:384622
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    References listed on IDEAS

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