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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

    as
    1. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    2. Wai-Yin Poon & Sik-Yum Lee, 1987. "Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 409-430, September.
    3. Julie S. Najita & Yi Li & Paul J. Catalano, 2009. "A novel application of a bivariate regression model for binary and continuous outcomes to studies of fetal toxicity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 555-573, September.
    4. de Leon, A.R., 2005. "Pairwise likelihood approach to grouped continuous model and its extension," Statistics & Probability Letters, Elsevier, vol. 75(1), pages 49-57, November.
    5. Mary Dupuis Sammel & Louise M. Ryan & Julie M. Legler, 1997. "Latent Variable Models for Mixed Discrete and Continuous Outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 667-678.
    6. Anders Skrondal & Sophia Rabe‐Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.
    7. D. R. Cox, 1972. "The Analysis of Multivariate Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 113-120, June.
    8. Gao, Xin & Song, Peter X.-K., 2010. "Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1531-1540.
    9. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
    10. D. R. Cox, 2004. "A note on pseudolikelihood constructed from marginal densities," Biometrika, Biometrika Trust, vol. 91(3), pages 729-737, September.
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