Atanu Bhattacharjee

Bayesian Approaches in Oncology Using R and OpenBUGS (eBook, ePUB)

• Format: ePub

Produktbeschreibung

The book focuses on building concepts and procedure to perform Bayesian in Oncology setup. It presents the roles of Bayesian in Oncology to clinical medicine in the context of substantive and current applications. This book will help readers interested in exploring, understanding, and solving oncology research queries through Bayesian.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 260
• Erscheinungstermin: 21. Dezember 2020
• Englisch
• ISBN-13: 9781000330069
• Artikelnr.: 60729044

Autorenporträt

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.

Inhaltsangabe

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