Written by a biostatistics expert with over 20 years of experience
in the field, Bayesian Methods in Epidemiology presents statistical
methods used in epidemiology from a Bayesian viewpoint. It employs
the software package WinBUGS to carry out the analyses and offers
the code in the text and for download online. The book examines
study designs that investigate the association between exposure to
risk factors and the occurrence of disease. It covers introductory
adjustment techniques to compare mortality between states and
regression methods to study the association between various risk
factors and disease, including logistic regression, simple and
multiple linear regression, categorical/ordinal regression, and
nonlinear models. The text also introduces a Bayesian approach for
the estimation of survival by life tables and illustrates other
approaches to estimate survival, including a parametric model based
on the Weibull distribution and the Cox proportional hazards
(nonparametric) model. Using Bayesian methods to estimate the lead
time of the modality, the author explains how to screen for a
disease among individuals that do not exhibit any symptoms of the
disease. With many examples and end-of-chapter exercises, this book
is the first to introduce epidemiology from a Bayesian perspective.
It shows epidemiologists how these Bayesian models and techniques
are useful in studying the association between disease and exposure
to risk factors.
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