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Published in: Lifetime Data Analysis 4/2023

15-08-2023

Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring

Authors: An-Min Tang, Nian-Sheng Tang, Dalei Yu

Published in: Lifetime Data Analysis | Issue 4/2023

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Abstract

We consider a novel class of semiparametric joint models for multivariate longitudinal and survival data with dependent censoring. In these models, unknown-fashion cumulative baseline hazard functions are fitted by a novel class of penalized-splines (P-splines) with linear constraints. The dependence between the failure time of interest and censoring time is accommodated by a normal transformation model, where both nonparametric marginal survival function and censoring function are transformed to standard normal random variables with bivariate normal joint distribution. Based on a hybrid algorithm together with the Metropolis–Hastings algorithm within the Gibbs sampler, we propose a feasible Bayesian method to simultaneously estimate unknown parameters of interest, and to fit baseline survival and censoring functions. Intensive simulation studies are conducted to assess the performance of the proposed method. The use of the proposed method is also illustrated in the analysis of a data set from the International Breast Cancer Study Group.

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Appendix
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Metadata
Title
Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring
Authors
An-Min Tang
Nian-Sheng Tang
Dalei Yu
Publication date
15-08-2023
Publisher
Springer US
Published in
Lifetime Data Analysis / Issue 4/2023
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI
https://doi.org/10.1007/s10985-023-09608-5

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