04155cam a2200577Mi 4500 9780429329449 FlBoTFG 20260210180824.0 m o d cr |n||||||||| 201212s2020 flua o 000 0 eng d OCoLC-P eng OCoLC-P 9781000329988 (electronic bk.) 1000329984 (electronic bk.) 9780429329449 (electronic bk.) 042932944X (electronic bk.) 9781000330069 (electronic bk. : EPUB) 1000330060 (electronic bk. : EPUB) 9781000330021 (electronic bk. : Mobipocket) 1000330028 (electronic bk. : Mobipocket) 9780367350505 0367350505 10.1201/9780429329449 doi (OCoLC)1226566683 (OCoLC)1228888728 (OCoLC-P)1226566683 QA279.5 MAT 029000 bisacsh MED 090000 bisacsh MED 062000 bisacsh MBNS bicssc 519.542 23 Bhattacharjee, Atanu, author. Bayesian approaches in oncology using R and OpenBUGS [electronic resource] / Atanu Bhattacharjee. Boca Raton : Chapman & Hall/CRC, 2020. 1 online resource text rdacontent still image rdacontent computer rdamedia online resource rdacarrier 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. OCLC-licensed vendor bibliographic record. Bayesian statistical decision theory. 793 Cancer Research Statistical methods. Oncology Research Statistical methods. R (Computer program language) Statistical methods. MATHEMATICS / Probability & Statistics / General bisacsh MEDICAL / Biostatistics bisacsh MEDICAL / Oncology bisacsh Taylor & Francis https://www.taylorfrancis.com/books/9780429329449 OCLC metadata license agreement http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 91961 91960