Bayesian Methods for Measures of Agreement Chapman & Hall/CRC Biostatistics Series
Auteur : Broemeling Lyle D.
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.
Introduction to Agreement. Bayesian Methods of Agreement for Two Raters. More Than Two Raters. Agreement and Correlated Observations. Modeling Patterns of Agreement. Agreement with Quantitative Scores. Sample Sizes for Agreement Studies. Bayesian Statistics. Appendices.
Lyle D. Broemeling
Date de parution : 12-2008
15.6x23.4 cm
Date de parution : 06-2020
15.6x23.4 cm
Thèmes de Bayesian Methods for Measures of Agreement :
Mots-clés :
BUGS CODE; Posterior Distribution; bayesian methods; Posterior Analysis; Credible Interval; Kappa coefficient; Joint Posterior Distribution; modeling patterns of agreement; Progressive Disease; likelihood function; Intraclass Correlation; sample size estimation; Von Eye; Posterior Median; Fi Ve; Linear Interaction Term; Constant Correlation Model; Kappa Coeffi Cient; Expected Cell Frequencies; Sample Size Formula; SUV Value; Conditional Kappa; Cell Frequencies; Geographic Atrophy; Fi Rst Population; Sample Size Choice; Fi Ve Readers; Variance Components; Δ00 Δ01 Δ02 Δ03 Δ0