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

An-Min Tang, Nian-Sheng Tang and Dalei Yu ()
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An-Min Tang: Yunnan University
Nian-Sheng Tang: Yunnan University
Dalei Yu: Xi’an Jiaotong University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2023, vol. 29, issue 4, No 9, 888-918

Abstract: 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.

Keywords: Dependent censoring; Joint model; Longitudinal data; Penalized-splines with linear constraints; Survival data; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10985-023-09608-5

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