000			02763cam a2200325 a 4500
001			17312879
003			BD-DhUL
005			20150809125700.0
008			120522s2012 flua b 001 0 eng
010			_a 2012014570
020			_a9781439872864 (hardback)
040			_aDLC _cDLC _dBD-DhUL
042			_apcc
050	0	0	_aQA279 _b.R59 2012
082	0	0	_a518 _223 _bRIJ
084			_aMAT029000 _aMED028000 _2bisacsh
100	1		_aRizopoulos, Dimitris.
245	1	0	_aJoint models for longitudinal and time-to-event data : _bwith applications in R / _cDimitris Rizopoulos.
260			_aBoca Raton : _bCRC Press, _c2012.
300			_axiv, 261 p. : _bill. ; _c24 cm.
365			_aGBP _b55.99
490	0		_aChapman & Hall/CRC biostatistics series ; _v6
504			_aIncludes bibliographical references and index.
520			_a"Preface Joint models for longitudinal and time-to-event data have become a valuable tool in the analysis of follow-up data. These models are applicable mainly in two settings: First, when focus is in the survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. Due to their capability to provide valid inferences in settings where simpler statistical tools fail to do so, and their wide range of applications, the last 25 years have seen many advances in the joint modeling field. Even though interest and developments in joint models have been widespread, information about them has been equally scattered in articles, presenting recent advances in the field, and in book chapters in a few texts dedicated either to longitudinal or survival data analysis. However, no single monograph or text dedicated to this type of models seems to be available. The purpose in writing this book, therefore, is to provide an overview of the theory and application of joint models for longitudinal and survival data. In the literature two main frameworks have been proposed, namely the random effects joint model that uses latent variables to capture the associations between the two outcomes (Tsiatis and Davidian, 2004), and the marginal structural joint models based on G estimators (Robins et al., 1999, 2000). In this book we focus in the former. Both subfields of joint modeling, i.e., handling of endogenous time-varying covariates and nonrandom dropout, are equally covered and presented in real datasets"-- _cProvided by publisher.
650		0	_aNumerical analysis _xData processing.
650		0	_aR (Computer program language)
650		7	_aMATHEMATICS / Probability & Statistics / General. _2bisacsh
650		7	_aMEDICAL / Epidemiology. _2bisacsh
942			_2ddc _cBK
999			_c41451 _d41451