Bayesian Approaches In Oncology Using R And Openbugs
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Bayesian Approaches in Oncology Using R and OpenBUGS
Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the foundation for later chapters. Many books on the bayesian approach and the statistical analysis are advanced, and many are theoretical. While most of them do cover the objective, the fact remains that data analysis can not be performed without actually doing it, and this means using dedicated statistical software. There are several software packages, all with their specific objective. Finally, all packages are free to use, are versatile with problem-solving, and are interactive with R and OpenBUGS. This book continues to cover a range of techniques related to oncology that grow in statistical analysis. It intended to make a single source of information on Bayesian statistical methodology for oncology research to cover several dimensions of statistical analysis. The book explains data analysis using real examples and includes all the R and OpenBUGS codes necessary to reproduce the analyses. The idea is to overall extending the Bayesian approach in oncology practice. It presents four sections to the statistical application framework: Bayesian in Clinical Research and Sample Size Calcuation Bayesian in Time-to-Event Data Analysis Bayesian in Longitudinal Data Analysis Bayesian in Diagnostics Test Statistics This book is intended as a first course in bayesian biostatistics for oncology students. An oncologist can find useful guidance for implementing bayesian in research work. It serves as a practical guide and an excellent resource for learning the theory and practice of bayesian methods for the applied statistician, biostatistician, and data scientist.
Bayesian Approaches in Oncology Using R and OpenBUGS
Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the foundation for later chapters. Many books on the bayesian approach and the statistical analysis are advanced, and many are theoretical. While most of them do cover the objective, the fact remains that data analysis can not be performed without actually doing it, and this means using dedicated statistical software. There are several software packages, all with their specific objective. Finally, all packages are free to use, are versatile with problem-solving, and are interactive with R and OpenBUGS. This book continues to cover a range of techniques related to oncology that grow in statistical analysis. It intended to make a single source of information on Bayesian statistical methodology for oncology research to cover several dimensions of statistical analysis. The book explains data analysis using real examples and includes all the R and OpenBUGS codes necessary to reproduce the analyses. The idea is to overall extending the Bayesian approach in oncology practice. It presents four sections to the statistical application framework: Bayesian in Clinical Research and Sample Size Calcuation Bayesian in Time-to-Event Data Analysis Bayesian in Longitudinal Data Analysis Bayesian in Diagnostics Test Statistics This book is intended as a first course in bayesian biostatistics for oncology students. An oncologist can find useful guidance for implementing bayesian in research work. It serves as a practical guide and an excellent resource for learning the theory and practice of bayesian methods for the applied statistician, biostatistician, and data scientist.
Applied Bayesian Statistics
Author: Mary Kathryn Cowles
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-01-04
This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results. In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output. Kate Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics. Her research areas are Bayesian and computational statistics, with application to environmental science. She is on the faculty of Statistics at The University of Iowa.