Pharmacokinetic-Pharmacodynamic Modeling and Simulation [electronic resource] / by Peter L. Bonate.
Tipo de material: TextoEditor: Boston, MA : Springer US : Imprint: Springer, 2011Edición: 2Descripción: XIX, 618 p. online resourceTipo de contenido:- text
- computer
- online resource
- 9781441994851
- SpringerLink (Online service)
- 615 23
- RM1-950
The 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.
Since 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 beenapplauded by students andteachersfor its readability and exposition of complex statisticaltopics. Using a building block approach, the text starts withlinear regression, nonlinear regression, and variance models at theindividual leveland then moves to population-level models withlinear and nonlinearmixed 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, andusesimulation to answer "what-if" questions. Scientists andstatisticians outside the pharmaceutical sciences will find the book invaluable as a reference forapplied modeling and simulation.
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