Modeling survival after acute myocardial infarction using accelerated failure time models and space varying regression
Date
2009-08-27T15:16:02Z
Authors
Yang, Aijun
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Abstract
Acute Myocardial Infarction (AMI), commonly known as heart attack, is a leading
cause of death for adult men and women in the world. Studying mortality after AMI
is therefore an important problem in epidemiology. This thesis develops statistical
methodology for examining geographic patterns in mortality following AMI. Specifically, we develop parametric Accelerated Failure Time (AFT) models for censored survival data, where space-varying regression is used to investigate spatial patterns of mortality after AMI. In addition to important covariates such as age and gender, the regression models proposed here also incorporate spatial random e ects that describe the residual heterogeneity associated with di erent local health geographical units. We conduct model inference under a hierarchical Bayesian modeling framework using Markov Chain Monte Carlo algorithms for implementation. We compare an array of models and address the goodness-of- t of the parametric AFT model through simulation studies and an application to a longitudinal AMI study in Quebec. The application of our AFT model to the Quebec AMI data yields interesting ndings
concerning aspects of AMI, including spatial variability. This example serves as a
strong case for considering the parametric AFT model developed here as a useful tool
for the analysis of spatially correlated survival data.
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Keywords
survival analysis, spatial analysis, MCMC methods, CAR model