Subject Variability (L1) 17 2.5.1 Additive Variation 17 2.5.2 Constant Coefficient of Variation 18 2.5.3 Exponential Variation 18 2.5.4 Modeling Sources of Between Preface xiii CHAPTER 1 The Practice of Pharmacometrics 1 1.1 Introduction 1 1.2 Applications of Sparse Data Analysis 2 1.3 Impact of Pharmacometrics 4 1.4 Clinical Example 5 CHAPTER 2 Population Model Concepts and Terminology 9 2.1 Introduction 9 2.2 Model Elements 10 2.3 Individual Subject Models 11 2.4 Population Models 12 2.4.1 FixedĮffect Parameters 14 2.5 Models of Random Between * Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects * Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and applications of empirical Bayesian estimates in the drug development environment * Includes detailed chapters on data set structure, developing control streams for modeling and simulation, model applications, interpretation of NONMEM output and results, and quality control * Has datasets, programming code, and practice exercises with solutions, available on a supplementary website This book provides a user-friendly, hands-on introduction to the Nonlinear Mixed Effects Modeling (NONMEM) system, the most powerful tool for pharmacokinetic / pharmacodynamic analysis.
An associated website hosts datasets and programming code.
Chapters discuss population model terminology, Bayesian analysis, PK/PD simulation. This useful guide helps pharmaceutical scientists and students learn the requisite information needed to perform mixed effect modeling of pharmacologic data using the NONMEM software package. Providing a user-friendly, hands-on introduction to the most powerful tool for population PK/PD models, Introduction to Population PK/PD Analysis with Nonlinear Mixed Effects Models introduces the reader to the NONMEM system, a powerful tool for pharmacokinetic / pharmacodynamic analysis.
0 kommentar(er)
