Pharmacokinetic-Pharmacodynamic Modeling and Simulation

This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models. Since the first edition was published the field has grown considerably in terms of maturity and technicality. The second edition of the book therefore considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and Principles of simulation. In addition, many of the other chapters have been expanded and updated. This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models.Since the first edition was published the field has grown considerably in terms of maturity and technicality. The second edition of the book therefore considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and Principles of simulation. In addition, many of the other chapters have been expanded and updated. 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 been championed 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 since the first edition. 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. Although the book is written towards pharmaceutical scientists, scientists, statisticians, and mathematicians outside the field will find the book invaluable in understanding complex statistical modeling and simulation.