Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 32050000000705 | RA652.2 .M3 B76 2014 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
Written by a biostatistics expert with over 20 years of experience in the field, Bayesian Methods in Epidemiologypresents 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.
ion 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.
Table of Contents
| Introduction to Bayesian Methods in Epidemiology |
| Introduction |
| Review of Statistical Methods in Epidemiology |
| Preview of the Book |
| Preview of the Appendices |
| A Bayesian Perspective of Association between Risk Exposure and Disease |
| Introduction |
| Incidence and Prevalence for Mortality and Morbidity |
| Association between Risk and Disease in Cohort Studies |
| Retrospective Studies: Association between Risk and Disease in Case-Control Studies |
| Cross-Sectional Studies |
| Attributable Risk |
| Bayesian Methods of Adjustment of Data |
| Introduction |
| The Direct Adjustment of Data |
| Indirect Standardization Adjustment |
| Stratification and Association between Disease and Risk Exposure |
| Mantel-Haenszel Estimator of Association |
| Matching to Adjust Data in Case-Control Studies |
| Regression Methods for Adjustment |
| Introduction |
| Logistic Regression |
| Linear Regression Models |
| Weighted Regression |
| Ordinal and Other Regression Models |
| A Bayesian Approach to Life Tables |
| Introduction |
| The Basic Life Table |
| Disease-Specific Life Tables |
| Life Tables for Medical Studies |
| Comparing Survival |
| The Kaplan-Meier Test |
| A Bayesian Approach to Survival Analysis |
| Introduction |
| Notation and Basic Table for Survival |
| Kaplan-Meier Survival Curves |
| Survival Analysis |
| Screening for Disease |
| Introduction |
| Principals of Screening |
| Evaluation of Screening Programs |
| The HIP Study (Health Insurance Plan of Greater New York) |
| Statistical Models for Epidemiology |
| Introduction |
| Review of Models for Epidemiology |
| Categorical Regression Models |
| Nonlinear Regression Models |
| Repeated Measures Model |
| Spatial Models for Epidemiology |
| Appendix A Introduction to Bayesian Statistics |
| Appendix B Introduction to WinBUGS |
| Index |
