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Review and Comparison of Computational Approaches for Joint Longitudinal and Time‐to‐Event Models

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  • Allison K.C. Furgal
  • Ananda Sen
  • Jeremy M.G. Taylor

Abstract

Joint models for longitudinal and time‐to‐event data are useful in situations where an association exists between a longitudinal marker and an event time. These models are typically complicated due to the presence of shared random effects and multiple submodels. As a consequence, software implementation is warranted that is not prohibitively time consuming. While methodological research in this area continues, several statistical software procedures exist to assist in the fitting of some joint models. We review the available implementation for frequentist and Bayesian models in the statistical programming languages R, SAS and Stata. A description of each procedure is given including estimation techniques, input and data requirements, available options for customisation and some available extensions, such as competing risks models. The software implementations are compared and contrasted through extensive simulation, highlighting their strengths and weaknesses. Data from an ongoing trial on adrenal cancer patients are used to study different nuances of software fitting on a practical example.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:istatr:v:87:y:2019:i:2:p:393-418
    DOI: 10.1111/insr.12322
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    Cited by:

    1. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.
    2. 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).
    3. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.

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