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Bayesian Semiparametric Joint Model for Longitudinal and Survival Data

Title: A Bayesian Semiparametric Joint Model for Longitudinal and Survival Data.
Name(s): Wang, Pengpeng, author
Slate, Elizabeth H., Professor Co-Directing Dissertation
Bradley, Jonathan R., Professor Co-Directing Dissertation
Wetherby, Amy M., University Representative
Lin, Lifeng, Committee Member
Florida State University, degree granting institution
College of Arts and Sciences, degree granting college
Department of Statistics, degree granting department
Type of Resource: text
Genre: Text
Doctoral Thesis
Issuance: monographic
Date Issued: 2019
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (112 pages)
Language(s): English
Abstract/Description: Many biomedical studies monitor both a longitudinal marker and a survival time on each subject under study. Modeling these two endpoints as joint responses has potential to improve the inference for both. We consider the approach of Brown and Ibrahim (2003) that proposes a Bayesian hierarchical semiparametric joint model. The model links the longitudinal and survival outcomes by incorporating the mean longitudinal trajectory as a predictor for the survival time. The usual parametric mixed effects model for the longitudinal trajectory is relaxed by using a Dirichlet process prior on the coefficients. A Cox proportional hazards model is then used for the survival time. The complicated joint likelihood increases the computational complexity. We develop a computationally efficient method by using a multivariate log-gamma distribution instead of Gaussian distribution to model the data. We use Gibbs sampling combined with Neal's algorithm (2000) and the Metropolis-Hastings method for inference. Simulation studies illustrate the procedure and compare this log-gamma joint model with the Gaussian joint models. We apply this joint modeling method to a human immunodeciency virus (HIV) data and a prostate-specific antigen (PSA) data.
Identifier: 2019_Spring_Wang_fsu_0071E_15120 (IID)
Submitted Note: A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Spring Semester 2019.
Date of Defense: April 16, 2019.
Keywords: Bayesian, Gibbs Sampler, Joint model, Longitudinal, Survival
Bibliography Note: Includes bibliographical references.
Advisory Committee: Elizabeth H. Slate, Professor Co-Directing Dissertation; Jonathan R. Bradley, Professor Co-Directing Dissertation; Amy M. Wetherby, University Representative; Lifeng Lin, Committee Member.
Subject(s): Statistics
Persistent Link to This Record:
Host Institution: FSU

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Wang, P. (2019). A Bayesian Semiparametric Joint Model for Longitudinal and Survival Data. Retrieved from