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Joint modeling of multivariate longitudinal mixed measurements and time to event data using a Bayesian approach

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  • T. Baghfalaki
  • M. Ganjali
  • D. Berridge

Abstract

In many longitudinal studies multiple characteristics of each individual, along with time to occurrence of an event of interest, are often collected. In such data set, some of the correlated characteristics may be discrete and some of them may be continuous. In this paper, a joint model for analysing multivariate longitudinal data comprising mixed continuous and ordinal responses and a time to event variable is proposed. We model the association structure between longitudinal mixed data and time to event data using a multivariate zero-mean Gaussian process. For modeling discrete ordinal data we assume a continuous latent variable follows the logistic distribution and for continuous data a Gaussian mixed effects model is used. For the event time variable, an accelerated failure time model is considered under different distributional assumptions. For parameter estimation, a Bayesian approach using Markov Chain Monte Carlo is adopted. The performance of the proposed methods is illustrated using some simulation studies. A real data set is also analyzed, where different model structures are used. Model comparison is performed using a variety of statistical criteria.

Suggested Citation

  • T. Baghfalaki & M. Ganjali & D. Berridge, 2014. "Joint modeling of multivariate longitudinal mixed measurements and time to event data using a Bayesian approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 1934-1955, September.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1934-1955
    DOI: 10.1080/02664763.2014.898132
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    References listed on IDEAS

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    1. Steffen Fieuws & Geert Verbeke, 2006. "Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 424-431, June.
    2. Yueh-Yun Chi & Joseph G. Ibrahim, 2006. "Joint Models for Multivariate Longitudinal and Multivariate Survival Data," Biometrics, The International Biometric Society, vol. 62(2), pages 432-445, June.
    3. Elizabeth R. Brown & Joseph G. Ibrahim, 2003. "A Bayesian Semiparametric Joint Hierarchical Model for Longitudinal and Survival Data," Biometrics, The International Biometric Society, vol. 59(2), pages 221-228, June.
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    1. Siamak Ghasemzadeh & Mojtaba Ganjali & Taban Baghfalaki, 2018. "Bayesian quantile regression for analyzing ordinal longitudinal responses in the presence of non-ignorable missingness," METRON, Springer;Sapienza Università di Roma, vol. 76(3), pages 321-348, December.
    2. M. H. Hof & J. Z. Musoro & R. B. Geskus & G. H. Struijk & I. J. M. ten Berge & A. H. Zwinderman, 2017. "Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2756-2777, November.
    3. Melkamu Molla Ferede & Samuel Mwalili & Getachew Dagne & Simon Karanja & Workagegnehu Hailu & Mahmoud El-Morshedy & Afrah Al-Bossly, 2022. "A Semiparametric Bayesian Joint Modelling of Skewed Longitudinal and Competing Risks Failure Time Data: With Application to Chronic Kidney Disease," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
    4. Taban Baghfalaki & Mojtaba Ganjali & Geert Verbeke, 2017. "A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2813-2836, November.
    5. Taban Baghfalaki & Mojtaba Ganjali, 2020. "A transition model for analyzing multivariate longitudinal data using Gaussian copula approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 169-223, June.

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