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

Thi Thu Pham Huong (), Pham Hoa () and Nur Darfiana ()
Additional contact information
Thi Thu Pham Huong: Mathematical Department, An Giang University, Vietnam National University, Ho Chi Minh City, Vietnam
Pham Hoa: Mathematical Department, An Giang University, Vietnam National University, Ho Chi Minh City, Vietnam
Nur Darfiana: CSEM, Flinders University, Flinders at Tonley, GPO BOX 2100, Adelaide5001, South Australia, Australia

Monte Carlo Methods and Applications, 2020, vol. 26, issue 1, 49-68

Abstract: Bayesian 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.

Keywords: Survival data; longitudinal data; joint models; Bayesian approach; random effects (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/mcma-2020-2058 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:26:y:2020:i:1:p:49-68:n:5

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html

DOI: 10.1515/mcma-2020-2058

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().