Bayesian Approaches in Oncology Using R and OpenBUGS

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

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.

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.