Type of Document Dissertation Author He, Jianghua URN etd-07092007-001422 Title Bayesian Dynamic Survival Models for Longitudinal Aging Data Degree Doctor of Philosophy Department Statistics, Department of Advisory Committee
Advisor Name Title Daniel L. McGee Committee Member Fred W. Huffer Committee Member Suzanne B. Johnson Committee Member Xufeng Niu Committee Member Keywords
- Bayesian Analysis
- Time-Varying Coefficient Model
- Survival Analysis
Date of Defense 2007-05-07 Availability unrestricted AbstractIn 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 timevarying
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.
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