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Simultaneous Bayesian modeling of longitudinal and survival data in breast cancer patients

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  • Ali Azarbar
  • Yu Wang
  • Saralees Nadarajah

Abstract

Using simultaneous Bayesian modeling, an attempt is made to analyze data on the size of lymphedema occurring in the arms of breast cancer patients after breast cancer surgery (as the longitudinal data) and the time interval for disease progression (as the time-to-event occurrence). A model based on a multivariate skew t distribution is shown to provide the best fit. This outcome was confirmed by simulation studies too.

Suggested Citation

  • Ali Azarbar & Yu Wang & Saralees Nadarajah, 2021. "Simultaneous Bayesian modeling of longitudinal and survival data in breast cancer patients," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(2), pages 400-414, January.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:2:p:400-414
    DOI: 10.1080/03610926.2019.1635701
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    Cited by:

    1. 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.

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