Bayesian Methods in Epidemiology

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A01=Lyle D. Broemeling
advanced Bayesian epidemiological modeling
analysis
Author_Lyle D. Broemeling
Bayesian approach for disease screening
Bayesian inference for epidemiology
Bayesian models and techniques for studying the association between disease and exposure to risk factors
biostatistics methods
bugs
BUGS CODE
Category=PBTB
Clinical Examination
code
Coronary Artery Calcium
Cox proportional hazards
Cox Proportional Hazards Model
credible
Credible Interval
density
Diagnostic Likelihood Ratios
disease screening techniques
distribution
epidemiology from a Bayesian viewpoint
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
error
Improper Prior Distribution
Incidence Rate
Initial Values List
interval
Interval Diagnosis
Lip Cancer
logistic regression models
MCMC Error
MH Estimator
MH Odds Ratio
models in epidemiology
monte
Multiple Linear Regression
Multiple Linear Regression Model
PH Assumption
posterior
Posterior Analysis
Posterior Density
Posterior Distribution
Recurrence Times
regression methods for Epidemiology
Regression Model
risk factor analysis
Roc Curve
SD Error
survival analysis and life tables
UK Trial
Weibull distribution survival
WinBUGS and epidemiology

Product details

  • ISBN 9781466564978
  • Weight: 1020g
  • Dimensions: 156 x 234mm
  • Publication Date: 13 Aug 2013
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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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