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Bayesian Approaches in Oncology Using R and OpenBUGS (Hardcover)
Loot Price: R3,251
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Bayesian Approaches in Oncology Using R and OpenBUGS (Hardcover)
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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.
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