Bayesian Approaches in Oncology Using R and OpenBUGS
- Available for pre-order. Item will ship after December 14, 2020
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.
Books on the bayesian approach and the statistical analysis -many 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, is versatile as problem-solving, and its interactivity 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, biostatisticians and data scientist.
Table of Contents
Part 1- Bayesian in Clinical Research
Chapter 1- Introduction to R and Open BUGS
Chapter 2- Sample size determination
Chapter 3- Study Design-I
Chapter 4- Study Design-II
Chapter 5- Optimum Biological Dose Selection
Part 2- Bayesian in Time-to-Event Data Analysis
Chapter 6- Survival Analysis
Chapter 7- Competing Risk Data Analysis
Chapter 8- Frailty Data Analysis
Chapter 9- Relative Survival Analysis
Part 3- Bayesian in Longitudinal Data Analysis
Chapter 10- Longitudinal Data Analysis
Chapter 11- Missing Data Analysis
Chapter 12- Joint Longitudinal and Survival Analysis
Chapter 13- Covariance modelling
Part 4- Bayesian in Diagnostics Test Statistics
Chapter 14- Bayesian Inference in Mixed-Effect Model
Chapter 15- Concordance Analysis
Chapter 16- High Dimensional Data Analysis
Atanu Bhattacharjee is an Assistant Professor at the Section of Biostatistics, Centre for Cancer Epidemiology, Tata Memorial Centre, India. He previously taught Biostatistics at the Malabar Cancer Centre, Kerala, India. He completed his PhD at Gauhati University, Assam, on Bayesian Statistical Inference. He is an elected member of the International Biometric Society (Indian Region). He served as Associate Editor of BMC Research Methodology. He has published over 200 research articles in various peer-reviewed journals.