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Bayesian Methods in Epidemiology
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Bayesian Methods in Epidemiology

by Lyle D. Broemeling , Lyle D. Broemeling

Book Details

Format Hardback or Cased Book
Book Series Chapman & Hall/CRC Biostatistics Series
ISBN-10 1466564970
ISBN-13 9781466564978
Publisher Taylor & Francis Inc
Imprint CRC Press Inc
Country of Manufacture US
Country of Publication GB
Publication Date Aug 13th, 2013
Print length 464 Pages
Weight 798 grams
Dimensions 24.30 x 15.60 x 2.60 cms
Product Classification: Bayesian inference
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Written by a biostatistics expert with over 20 years of experience in the field, this book is the first to introduce epidemiology from a Bayesian perspective. It shows epidemiologists how Bayesian models and techniques are useful in studying the association between disease and exposure to risk factors. With many examples and end-of-chapter exercises, the book employs the software package WinBUGS to carry out the analyses and offers the code in the text and for download online.

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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