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Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues

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  • Lang Wu
  • Wei Liu
  • Grace Y. Yi
  • Yangxin Huang

Abstract

In the past two decades, joint models of longitudinal and survival data have received much attention in the literature. These models are often desirable in the following situations: (i) survival models with measurement errors or missing data in time-dependent covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process and a longitudinal process are associated via latent variables. In these cases, separate inferences based on the longitudinal model and the survival model may lead to biased or inefficient results. In this paper, we provide a brief overview of joint models for longitudinal and survival data and commonly used methods, including the likelihood method and two-stage methods.

Suggested Citation

  • Lang Wu & Wei Liu & Grace Y. Yi & Yangxin Huang, 2012. "Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues," Journal of Probability and Statistics, Hindawi, vol. 2012, pages 1-17, December.
    • Handle: RePEc:hin:jnljps:640153
    DOI: 10.1155/2012/640153
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    Cited by:

    1. 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.
    2. Atanu B & Gajendra V & Jesna J & Ramesh V, 2017. "Multiple Imputations for Determining an Optimum Biological Dose of a Metronomic Chemotherapy," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 3(5), pages 129-140, October.
    3. Maaya, Leonard & Meulders, Michel & Vandebroek, Martina, 2021. "Joint analysis of preferences and drop out data in discrete choice experiments," Journal of choice modelling, Elsevier, vol. 41(C).

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