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The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC

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  • Rizopoulos, Dimitris

Abstract

Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markov chain Monte Carlo algorithms. JMbayes can fit a wide range of joint models, including among others joint models for continuous and categorical longitudinal responses, and provides several options for modeling the association structure between the two outcomes. In addition, this package can be used to derive dynamic predictions for both outcomes, and offers several tools to validate these predictions in terms of discrimination and calibration. All these features are illustrated using a real data example on patients with primary biliary cirrhosis.

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  • Rizopoulos, Dimitris, 2016. "The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i07).
    • Handle: RePEc:jss:jstsof:v:072:i07
    DOI: http://hdl.handle.net/10.18637/jss.v072.i07
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    References listed on IDEAS

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    1. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    2. Jeremy M. G. Taylor & Yongseok Park & Donna P. Ankerst & Cecile Proust-Lima & Scott Williams & Larry Kestin & Kyoungwha Bae & Tom Pickles & Howard Sandler, 2013. "Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models," Biometrics, The International Biometric Society, vol. 69(1), pages 206-213, March.
    3. Anonymous, 2014. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 9(1), pages 1-2, May.
    4. Guo X. & Carlin B.P., 2004. "Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages," The American Statistician, American Statistical Association, vol. 58, pages 16-24, February.
    5. Anonymous, 2014. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 9(2), pages 109-110, August.
    6. Dimitris Rizopoulos & Laura A. Hatfield & Bradley P. Carlin & Johanna J. M. Takkenberg, 2014. "Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1385-1397, December.
    7. Elizabeth R. Brown & Joseph G. Ibrahim & Victor DeGruttola, 2005. "A Flexible B-Spline Model for Multiple Longitudinal Biomarkers and Survival," Biometrics, The International Biometric Society, vol. 61(1), pages 64-73, March.
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    Cited by:

    1. Zhang, Danjie & Chen, Ming-Hui & Ibrahim, Joseph G. & Boye, Mark E. & Shen, Wei, 2016. "JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i03).
    2. Kamaryn T. Tanner & Linda D. Sharples & Rhian M. Daniel & Ruth H. Keogh, 2021. "Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 3-30, January.
    3. Brilleman, Samuel L. & Wolfe, Rory & Moreno-Betancur, Margarita & Sales, Anne E. & Langa, Kenneth M. & Li, Yun & Daugherty Biddison, Elizabeth L. & Rubinson, Lewis & Iwashyna, Theodore J., 2017. "Associations between community-level disaster exposure and individual-level changes in disability and risk of death for older Americans," Social Science & Medicine, Elsevier, vol. 173(C), pages 118-125.
    4. Wang, Shikun & Li, Zhao & Lan, Lan & Zhao, Jieyi & Zheng, W. Jim & Li, Liang, 2022. "GPU accelerated estimation of a shared random effect joint model for dynamic prediction," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    5. Murray, James & Philipson, Pete, 2022. "A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).

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