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A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data

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  • Murray, James
  • Philipson, Pete

Abstract

Joint models are an increasingly popular way to characterise the relationship between one or more longitudinal responses and an event of interest. However, for multivariate joint models the increased dimensionality and complexity of random effects present in the model specification are commensurate with increased computing time, hampering the implementation of many classic approaches. An approximate EM algorithm which ameliorates the so-called ‘curse of dimensionality’ is developed. The scaleability and accuracy of the proposed method are demonstrated via two simulation studies and applied to data arising from two clinical trials in the disease areas of cirrhosis and Alzheimer's disease, each with three biomarkers.

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  • 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).
    • Handle: RePEc:eee:csdana:v:170:y:2022:i:c:s0167947322000184
    DOI: 10.1016/j.csda.2022.107438
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    References listed on IDEAS

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    1. Philipson, Pete & Hickey, Graeme L. & Crowther, Michael J. & Kolamunnage-Dona, Ruwanthi, 2020. "Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    2. Hongsheng Dai & Jianxin Pan, 2018. "Joint Modelling of Survival and Longitudinal Data with Informative Observation Times," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(3), pages 571-589, September.
    3. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    4. Peng, Mengjiao & Xiang, Liming & Wang, Shanshan, 2018. "Semiparametric regression analysis of clustered survival data with semi-competing risks," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 53-70.
    5. Allison K.C. Furgal & Ananda Sen & Jeremy M.G. Taylor, 2019. "Review and Comparison of Computational Approaches for Joint Longitudinal and Time‐to‐Event Models," International Statistical Review, International Statistical Institute, vol. 87(2), pages 393-418, August.
    6. Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre, 2009. "Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 637-654, June.
    7. Khurshid Alam & Arnab Maity & Sanjoy K. Sinha & Dimitris Rizopoulos & Abdus Sattar, 2021. "Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 64-90, January.
    8. 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).
    9. Bernhardt, Paul W. & Zhang, Daowen & Wang, Huixia Judy, 2015. "A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 37-53.
    10. Yue Wang & Joseph G. Ibrahim & Hongtu Zhu, 2020. "Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study," Biometrics, The International Biometric Society, vol. 76(4), pages 1109-1119, December.
    11. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
    12. Baghishani, Hossein & Mohammadzadeh, Mohsen, 2012. "Asymptotic normality of posterior distributions for generalized linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 66-77.
    13. Lisa M. McCrink & Adele H. Marshall & Karen J. Cairns, 2013. "Advances in Joint Modelling: A Review of Recent Developments with Application to the Survival of End Stage Renal Disease Patients," International Statistical Review, International Statistical Institute, vol. 81(2), pages 249-269, August.
    14. Hwang, Yi-Ting & Tsai, Hao-Yun & Chang, Yeu-Jhy & Kuo, Hsun-Chih & Wang, Chun-Chao, 2011. "The joint model of the logistic model and linear random effect model -- An application to predict orthostatic hypertension for subacute stroke patients," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 914-923, January.
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    Cited by:

    1. Murray, James & Philipson, Pete, 2023. "Fast estimation for generalised multivariate joint models using an approximate EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    2. Zhang, Zili & Charalambous, Christiana & Foster, Peter, 2023. "A Gaussian copula joint model for longitudinal and time-to-event data with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

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