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Bayesian Dynamic Survival Models for Longitudinal Aging Data

Title: Bayesian Dynamic Survival Models for Longitudinal Aging Data.
Name(s): He, Jianghua, author
McGee, Daniel L., professor co-directing dissertation
Niu, Xufeng, professor co-directing dissertation
Johnson, Suzanne B., outside committee member
Huffer, Fred W., committee member
Department of Statistics, degree granting department
Florida State University, degree granting institution
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2007
Publisher: Florida State University
Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: In this study, we will examine the Bayesian Dynamic Survival Models, time-varying coefficients models from a Bayesian perspective, and their applications in the aging setting. The specific questions we are interested in are: Do the relative importance of characteristics measured at a particular age, such as blood pressure, smoking, and body weight, with respect to heart diseases or death change as people age? If they do, how can we model the change? And, how does the change affect the analysis results if fixed-effect models are applied? In the epidemiological and statistical literature, the relationship between a risk factor and the risk of an event is often described in terms of the numerical contribution of the risk factor to the total risk within a follow-up period, using methods such as contingency tables and logistic regression models. With the development of survival analysis, another method named the Proportional Hazards Model becomes more popular. This model describes the relationship between a covariate and risk within a follow-up period as a process, under the assumption that the hazard ratio of the covariate is fixed during the follow-up period. Neither previous methods nor the Proportional Hazards Model allows the effect of a covariates to change flexibly with time. In these study, we intend to investigate some classic epidemiological relationships using appropriate methods that allow coefficients to change with time, and compare our results with those found in the literature. After describing what has been done in previous work based on multiple logistic regression or discriminant function analysis, we summarize different methods for estimating the time varying coefficient survival models that are developed specifically for the situations under which the proportional hazards assumption is violated. We will focus on the Bayesian Dynamic Survival Model because its flexibility and Bayesian structure fits our study goals. There are two estimation methods for the Bayesian Dynamic Survival Models, the Linear Bayesian Estimation (LBE) method and the Markov Chain Monte Carlo (MCMC) sampling method. The LBE method is simpler, faster, and more flexible to calculate, but it requires specifications of some parameters that usually are unknown. The MCMC method gets around the difficulty of specifying parameters, but is much more computationally intensive. We will use a simulation study to investigate the performances of these two methods, and provide suggestions on how to use them effectively in application. The Bayesian Dynamic Survival Model is applied to the Framingham Heart Study to investigate the time-varying effects of covariates such as gender, age, smoking, and SBP (Systolic Blood Pressure) with respect to death. We also examined the changing relationship between BMI (Body Mass Index) and all-cause mortality, and suggested that some of the heterogeneity observed in the results found in the literature is likely to be a consequence of using fixed effect models to describe a time-varying relationship.
Identifier: FSU_migr_etd-4174 (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: Summer Semester, 2007.
Date of Defense: May 7, 2005.
Keywords: Bayesian Analysis, Time-Varying Coefficient Model, Survival Analysis
Bibliography Note: Includes bibliographical references.
Advisory Committee: Daniel L. McGee, Professor Co-Directing Dissertation; Xufeng Niu, Professor Co-Directing Dissertation; Suzanne B. Johnson, Outside Committee Member; Fred W. Huffer, Committee Member.
Subject(s): Statistics
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Host Institution: FSU

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He, J. (2007). Bayesian Dynamic Survival Models for Longitudinal Aging Data. Retrieved from