000			04121cam a2200565Mi 4500
001			9780429329449
003			FlBoTFG
005			20220509193059.0
006			m o d
007			cr |n|||||||||
008			201212s2020 flua o 000 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781000329988 _q(electronic bk.)
020			_a1000329984 _q(electronic bk.)
020			_a9780429329449 _q(electronic bk.)
020			_a042932944X _q(electronic bk.)
020			_a9781000330069 _q(electronic bk. : EPUB)
020			_a1000330060 _q(electronic bk. : EPUB)
020			_a9781000330021 _q(electronic bk. : Mobipocket)
020			_a1000330028 _q(electronic bk. : Mobipocket)
020			_z9780367350505
020			_z0367350505
024	7		_a10.1201/9780429329449 _2doi
035			_a(OCoLC)1226566683 _z(OCoLC)1228888728
035			_a(OCoLC-P)1226566683
050		4	_aQA279.5
072		7	_aMAT _x029000 _2bisacsh
072		7	_aMED _x090000 _2bisacsh
072		7	_aMED _x062000 _2bisacsh
072		7	_aMBNS _2bicssc
082	0	4	_a519.542 _223
100	1		_aBhattacharjee, Atanu, _eauthor.
245	1	0	_aBayesian approaches in oncology using R and OpenBUGS _h[electronic resource] / _cAtanu Bhattacharjee.
264		1	_aBoca Raton : _bChapman & Hall/CRC, _c2020.
300			_a1 online resource
336			_atext _2rdacontent
336			_astill image _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
520			_aBayesian 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.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aBayesian statistical decision theory.
650		0	_aCancer _xResearch _xStatistical methods.
650		0	_aOncology _xResearch _xStatistical methods.
650		0	_aR (Computer program language) _xStatistical methods.
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
650		7	_aMEDICAL / Biostatistics _2bisacsh
650		7	_aMEDICAL / Oncology _2bisacsh
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429329449
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c129412 _d129412