000 04355nam a22005655i 4500
001 978-0-387-72835-3
003 DE-He213
005 20160302163225.0
007 cr nn 008mamaa
008 100301s2008 xxu| s |||| 0|eng d
020 _a9780387728353
_9978-0-387-72835-3
024 7 _a10.1007/978-0-387-72835-3
_2doi
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
_2bicssc
072 7 _aPBWL
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.2
_223
100 1 _aDuchateau, Luc.
_eauthor.
245 1 4 _aThe Frailty Model
_h[electronic resource] /
_cby Luc Duchateau, Paul Janssen.
264 1 _aNew York, NY :
_bSpringer New York,
_c2008.
300 _aXVII, 316 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aParametric proportional hazards models with gamma frailty -- Alternatives for the frailty model -- Frailty distributions -- The semiparametric frailty model -- Multifrailty and multilevel models -- Extensions of the frailty model.
520 _aClustered survival data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. Frailty models provide a powerful tool to analyse clustered survival data. In contrast to the large number of research publications on frailty models, relatively few statistical software packages contain frailty models. It is demanding for statistical practitioners and graduate students to grasp a good knowledge on frailty models from the existing literature. This book provides an in-depth discussion and explanation of the basics of frailty model methodology for such readers. The discussion includes parametric and semiparametric frailty models and accelerated failure time models. Common techniques to fit frailty models include the EM-algorithm, penalised likelihood techniques, Laplacian integration and Bayesian techniques. More advanced frailty models for hierarchical data are also included. Real-life examples are used to demonstrate how particular frailty models can be fitted and how the results should be interpreted. The programs to fit all the worked-out examples in the book are available from the Springer website with most of the programs developed in the freeware packages R and Winbugs. The book starts with a brief overview of some basic concepts in classical survival analysis, collecting what is needed for the reading on the more complex frailty models. Luc Duchateau is Associate Professor of Statistics at the Faculty of Veterinary Medicine of the Ghent University, Belgium. He is board member of the Quetelet Society (Belgian Region of the International Biometric Society) and of the International Biometric Society Channel Network. He has collaborated extensively with physicians in oncology and allergy, public health workers and veterinarians, and is an author of numerous papers in statistical, medical and veterinarian journals. Paul Janssen is Professor of Statistics at the Centre for Statistics of the Hasselt University, Diepenbeek, Belgium. He is an elected member of the International Statistical Institute. He spent research visits at the Johns Hopkins University (Baltimore, USA) and the University of Washington (Seattle, USA). His research interests include survival analysis, nonparametric estimation, resampling techniques and asymptotic theory.
650 0 _aMathematics.
650 0 _aCancer research.
650 0 _aInfectious diseases.
650 0 _aComputer simulation.
650 0 _aBiometrics (Biology).
650 0 _aProbabilities.
650 0 _aStatistics.
650 1 4 _aMathematics.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
650 2 4 _aCancer Research.
650 2 4 _aSimulation and Modeling.
650 2 4 _aBiometrics.
650 2 4 _aInfectious Diseases.
700 1 _aJanssen, Paul.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387728346
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-387-72835-3
912 _aZDB-2-SMA
999 _c180264
_d180264