scholarly journals Penalized Spline Joint Models for Longitudinal and Time-To-Event Data

2018 ◽  
Author(s):  
Huong Thi Thu Pham ◽  
Hoa Pham
Keyword(s):  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Penalized Spline ◽  
Time To Event Data

A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis

2020 ◽  
Vol 26 (1) ◽  
pp. 49-68
Author(s):  
Huong Thi Thu Pham ◽  
Hoa Pham ◽  
Darfiana Nur
Keyword(s):  
Sensitivity Analysis ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Bayesian Approaches ◽  
Penalized Spline ◽  
Time To Event Data ◽  
Fully Bayesian ◽  
The Impact

AbstractBayesian approaches have been used in the literature to estimate the parameters for joint models of longitudinal and time-to-event data. The main aim of this paper is to analyze the impact of prior distributions on estimating parameters in a proposed fully Bayesian analysis setting for the penalized spline joint models. To achieve this aim, the joint posterior distribution of parameters in survival and longitudinal submodels is presented. The Markov chain Monte Carlo (MCMC) algorithm is then proposed, which consists of the Gibbs sampler (GS) and Metropolis Hastings (MH) algorithms to sample for the target conditional posterior distributions. The prior sensitivity analysis for the baseline hazard rate and association parameters is performed through simulation studies and a case study.

scholarly journals Penalized spline joint models for longitudinal and time-to-event data

2016 ◽  
Vol 46 (20) ◽  
pp. 10294-10314 ◽  
Author(s):  
Pham Thi Thu Huong ◽  
Darfiana Nur ◽  
Alan Branford
Keyword(s):  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Penalized Spline ◽  
Time To Event Data

A Bayesian mixture of semiparametric mixed-effects joint models for skewed-longitudinal and time-to-event data

2015 ◽  
Vol 34 (20) ◽  
pp. 2820-2843 ◽  
Author(s):  
Jiaqing Chen ◽  
Yangxin Huang
Keyword(s):  
Mixed Effects ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Time To Event Data ◽  
Bayesian Mixture

Bayesian Change-Point Joint Models for Multivariate Longitudinal and Time-to-Event Data

2020 ◽  
pp. 1-15
Author(s):  
Jiaqing Chen ◽  
Yangxin Huang ◽  
Nian-Sheng Tang
Keyword(s):  
Change Point ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Time To Event Data

scholarly journals Correction to: joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Maria Sudell ◽  
Ruwanthi Kolamunnage-Dona ◽  
Catrin Tudur-Smith
Keyword(s):  
Meta Analysis ◽  
Reporting Quality ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Time To Event Data

Joint Models for Longitudinal and Time-to-Event Data

2012 ◽  
Author(s):  
Dimitris Rizopoulos
Keyword(s):  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Time To Event Data

scholarly journals Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Maria Sudell ◽  
Ruwanthi Kolamunnage-Dona ◽  
Catrin Tudur-Smith
Keyword(s):  
Meta Analysis ◽  
Reporting Quality ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Time To Event Data
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