000 03869nam a22004575i 4500
001 978-1-4614-2035-4
003 DE-He213
005 20140220083244.0
007 cr nn 008mamaa
008 111117s2012 xxu| s |||| 0|eng d
020 _a9781461420354
_9978-1-4614-2035-4
024 7 _a10.1007/978-1-4614-2035-4
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aBeyersmann, Jan.
_eauthor.
245 1 0 _aCompeting Risks and Multistate Models with R
_h[electronic resource] /
_cby Jan Beyersmann, Arthur Allignol, Martin Schumacher.
264 1 _aNew York, NY :
_bSpringer New York,
_c2012.
300 _aXI, 245p. 49 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUse R!
505 0 _aData examples -- An informal introduction to hazard-based analyses -- Competing risks -- Multistate modelling of competing risks --  Nonparametric estimation -- Proportional hazards models -- Nonparametric hypothesis testing -- Further topics in competing risks -- Multistate models and their connection to competing risks -- Nonparametric estimation -- Proportional transition hazards models -- Time-dependent covariates and multistate models -- Further topics in multistate modeling.
520 _aCompeting Risks and Multistate Models with R covers models that generalize the analysis of time to a single event (survival analysis) to analyzing the timing of distinct terminal events (competing risks) and possible intermediate events (multistate models). Both R and multistate methods are promoted with a focus on non- and semiparametric methods.   This book explains hazard-based analyses of competing risks and multistate data with R. Special emphasis is placed on the interpretation of the results. A unique feature of this book is that readers are encouraged to simulate their own data based on the transition hazards only, which are the key quantities of the subsequent analyses. This simulation-based approach is supplemented with real data examples from studies in clinical medicine where the authors have been involved.   This book is aimed at data analysts, with a background in standard survival analysis, who wish to understand, analyse and interpret more complex event histories with R. It is also suitable for graduate courses in biostatistics, statistics and epidemiological methods. The real data examples, R packages, and the entire R code used in the book are available online.   The authors are affiliated with the Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg and the Freiburg Center for Data Analysis and Modelling, University of Freiburg, Germany.  Jan Beyersmann is Senior Statistician and serves on the editorial board of Statistics in Medicine. Arthur Allignol is Statistician and has contributed several R packages on competing risks and multistate models.  Martin Schumacher is Professor of Biostatistics and Director of the Institute of Medical Biometry and Medical Informatics, Freiburg.  He has been involved in theoretical developments as well as in practical applications of survival analyses and their extensions over many years.
650 0 _aStatistics.
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
700 1 _aAllignol, Arthur.
_eauthor.
700 1 _aSchumacher, Martin.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461420347
830 0 _aUse R!
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-2035-4
912 _aZDB-2-SMA
999 _c101185
_d101185