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020 _z9780429622557
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024 8 _a10.1201/9780429054167
_2doi
035 _a(OCoLC)1245418671
035 _a(OCoLC-P)1245418671
050 4 _aR853.S7
_bL44 2021
072 7 _aMAT
_x029000
_2bisacsh
072 7 _aTEC
_x010000
_2bisacsh
072 7 _aMBNS
_2bicssc
082 0 4 _a610.72/7
_223
100 1 _aLegrand, Catherine,
_eauthor.
245 1 0 _aAdvanced survival models /
_cCatherine Legrand.
250 _aFirst edition.
264 1 _aBoca Raton :
_bCRC Press,
_c2021.
264 4 _c©2021
300 _a1 online resource (xxviii, 332 pages) :
_billustrations (black and white).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aChapman & Hall/CRC biostatistics series
500 _a"A Chapman & Hall book"
520 _aSurvival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aMedicine
_xResearch
_xStatistical methods.
650 7 _aMATHEMATICS / Probability & Statistics / General
_2bisacsh
650 7 _aTECHNOLOGY / Environmental Engineering & Technology
_2bisacsh
856 4 0 _3Read Online
_uhttps://www.taylorfrancis.com/books/9780429054167
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _2lcc
_cEBK
999 _c16351
_d16351