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

Ali Azarbar; Yu Wang; Saralees Nadarajah

doi:10.1080/03610926.2019.1635701

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

Ali Azarbar, Yu Wang, Saralees Nadarajah^*

^*Corresponding author for this work

Department of Mathematics

Amir Kabir University of Technology

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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.

Original language	English
Journal	Communications in Statistics - Theory and Methods
Early online date	2 Jul 2019
DOIs	https://doi.org/10.1080/03610926.2019.1635701
Publication status	Published - 2019

Keywords

Longitudinal and survival data
MCMC method
Simultaneous Bayesian modeling
Time-to-event

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1080/03610926.2019.1635701

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Cite this

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title = "Simultaneous Bayesian modeling of longitudinal and survival data in breast cancer patients",

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.",

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AU - Azarbar, Ali

AU - Wang, Yu

AU - Nadarajah, Saralees

PY - 2019

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N2 - 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.

AB - 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.

KW - Longitudinal and survival data

KW - MCMC method

KW - Simultaneous Bayesian modeling

KW - Time-to-event

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DO - 10.1080/03610926.2019.1635701

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SN - 0361-0926

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

ER -

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

Abstract

Keywords

UN SDGs

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