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More About This Textbook
Overview
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.
The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software.
Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation. With examples throughout and end-of-chapter exercises, it discusses how to successfully design and analyze an agreement study.
Editorial Reviews
From the Publisher
"This book is a welcome addition to the literature on Bayesian inference as it presents methods for the design and analysis of agreement studies. … The approach presented by the author is novel and the novice will find a helpful introduction to Bayesian inference in an appendix. … The text is readable and will form a valuable reference source. For those unfamiliar with WinBUGS, the author introduces the fundamentals of programming and executing BUGS."—International Statistical Review, 2010
"This book deals with measures of agreement from a Bayesian perspective, focusing mainly on variants of Cohen’s κ, but also other measures included in Shoukri (2003) and von Eye and Mun (2005), frequentist texts for which this book is intended to be a Bayesian companion. Dr. Broemeling uses examples throughout the book to illustrate concepts rather than resorting to jargon … This book would be valuable for those using the methods in Shoukri and von Eye and Mun. …"
—Journal of the Royal Statistical Society, Series A, Volume 173, Issue 1, January 2010
Product Details
Related Subjects
Table of Contents
Introduction to Agreement
Introduction
Agreement and Statistics
The Bayesian Approach
Some Examples of Agreement
Sources of Information
Software and Computing
A Preview of the Book
Bayesian Methods of Agreement for Two Raters
Introduction
The Design of Agreement Studies
Precursors of Kappa
Chance Corrected Measures of Agreement
Conditional Kappa
Kappa and Stratification
Weighted Kappa
Intraclass Kappa
Other Measures of Agreement
Agreement with a Gold Standard
Kappa and Association
Consensus
More Than Two Raters
Introduction
Kappa with Many Raters
Partial Agreement
Stratified Kappa
Intraclass Kappa
The Fleiss Generalized Kappa
The G Coefficient and Other Indices
Kappa and Homogeneity
Introduction to Model-Based Approaches
Agreement and Matching
Agreement and Correlated Observations
Introduction
An Example of Paired Observations
The Oden Pooled Kappa and Schouten Weighted Kappa
A Generalized Correlation Model
The G Coefficient and Other Indices of Agreement
Homogeneity with Dependent Data
Logistic Regression and Agreement
Modeling Patterns of Agreement
Introduction
Nominal Responses
Ordinal Responses
More than Two Raters
Other Methods for Patterns of Agreement
Summary of Modeling and Agreement
Agreement with Quantitative Scores
Introduction
Regression and Correlation
The Analysis of Variance
Intraclass Correlation Coefficient for Agreement
With Covariates
Other Considerations with Continuous Scores
Sample Sizes for Agreement Studies
Introduction
The Classical and Bayesian Approaches to Power Analysis
The Standard Populations: Classical and Bayesian Approaches
Kappa, the G Coefficient, and Other Indices
The Logistic Linear Model
Regression and Correlation
The Intraclass Correlation
Bayesian Approaches to Sample Size
Appendix A: Bayesian Statistics
Introduction
Bayes Theorem
Prior Information
Posterior Information
Inference
Predictive Inference
Checking Model Assumptions
Sample Size Problems
Computing
Appendix B: Introduction to WinBUGS
Introduction
Download
The Essentials
Execution
Output
Examples
Summary
Exercises appear at the end of each chapter.