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008			120720s2012 fluad sb 001 0 eng d
020			_a9781439872871 (ebook : PDF)
040			_aBD-DhSAU _cBD-DhSAU
090			_aQA279 _b.R59 2012
092			_a518 _bR627
100	1		_aRizopoulos, Dimitris.
245	1	0	_aJoint models for longitudinal and time-to-event data _h[electronic resource] : _bwith applications in R / _cDimitris Rizopoulos.
260			_aBoca Raton : _bCRC Press, _c2012.
300			_axiv, 261 p. : _bill.
490	1		_aChapman & Hall/CRC biostatistics series ; _v6
500			_a"A Chapman & Hall book."
504			_aIncludes bibliographical references (p. 239-255) and index.
505	0		_a1. Introduction -- 2. Longitudinal data analysis -- 3. Analysis of event time data -- 4. Joint models for longitudinal and time-to-event data -- 5. Extensions of the standard joint model -- 6. Joint model diagnostics -- 7. Prediction and accuracy in joint models.
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.
530			_aAlso available in print edition.
538			_aMode of access: World Wide Web.
650		0	_aNumerical analysis _xData processing.
650		0	_aR (Computer program language)
655		7	_aElectronic books. _2lcsh
776	1		_z9781439872864 (hardback)
830		0	_aChapman & Hall/CRC biostatistics series ; _v6.
856	4	0	_uhttp://marc.crcnetbase.com/isbn/9781439872871 _qapplication/PDF
999			_c12197 _d12196