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007			cr nn 008mamaa
008			110629s2011 xxu| s |||| 0|eng d
020			_a9781441994851 _9978-1-4419-9485-1
040			_cMX-MeUAM
050		4	_aRM1-950
082	0	4	_a615 _223
100	1		_aBonate, Peter L. _eauthor.
245	1	0	_aPharmacokinetic-Pharmacodynamic Modeling and Simulation _h[recurso electrónico] / _cby Peter L. Bonate.
250			_a2.
264		1	_aBoston, MA : _bSpringer US : _bImprint: Springer, _c2011.
300			_aXIX, 618 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aThe Art of Modeling -- Linear Models and Regression -- Nonlinear Models and Regression -- Variance Models, Weighting, and Transformations -- Case Studies in Linear and Nonlinear Modeling -- Linear Mixed Effects Models -- Nonlinear Mixed Effects Models: Theory -- Nonlinear Mixed Effects Models: Practical Issues -- Nonlinear Mixed Effects Models: Case Studies -- Bayesian Modeling -- Generalized Linear Models and Its Extensions -- Principles of Simulation -- Appendix -- Index.
520			_aSince its publication in 2006, Pharmacokinetic-Pharmacodynamic Modeling and Simulation has become the leading text on modeling of pharmacokinetic and pharmacodynamic data using nonlinear mixed effects models and has been applauded by students and teachers for its readability and exposition of complex statistical topics. Using a building block approach, the text starts with linear regression, nonlinear regression, and variance models at the individual level and then moves to population-level models with linear and nonlinear mixed effects models.  Particular emphasis is made highlighting relationships between the model types and how the models build upon one another.  With the second edition, new chapters on generalized nonlinear mixed effects models and Bayesian models are presented, along with an extensive chapter on simulation.  In addition, many chapters have been updated to reflect recent developments.  The theory behind the methods is illustrated using real data from the literature and from the author's experiences in drug development.  Data are analyzed using a variety of software, including NONMEM, SAS, SAAM II, and WinBUGS.  A key component of the book is to show how models are developed using an acceptance-rejection paradigm with the ultimate goal of using models to explain data, summarize complex experiments, and use simulation to answer "what-if" questions. Scientists and statisticians outside the pharmaceutical sciences will find the book invaluable as a reference for applied modeling and simulation.
650		0	_aMedicine.
650		0	_aToxicology.
650	1	4	_aBiomedicine.
650	2	4	_aPharmacology/Toxicology.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781441994844
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-4419-9485-1
596			_a19
942			_cLIBRO_ELEC
999			_c200118 _d200118