Mixed Effects Models For The Population Approach - Lavielle Marc | Libro Chapman And Hall/Crc 08/2014 - HOEPLI.it

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Mixed Effects Models for the Population Approach Models, Tasks, Methods and Tools

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Lingua: Inglese
Pubblicazione: 08/2014
Edizione: 1° edizione

Note Editore

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data ModelingThe book first shows how the framework allows model representation for different data types, including continuous, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM algorithm (SAEM), for modeling these diverse data types. The book also covers other essential methods, including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix, and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical ImplementationThis book takes readers through the whole modeling process, from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs.


Introduction and Preliminary ConceptsOverviewThe population approach About models Tasks, methods and tools Contents of the book Mixed Effects Models vs Hierarchical ModelsFrom linear models to nonlinear mixed effects models .From nonlinear mixed effects models to hierarchical models From generalized mixed models to hierarchical models What Is a Model? A Joint Probability Distribution!Introduction and notation An illustrative example Using a model for executing tasks Implementing hierarchical models with Mlxtran Defining ModelsModeling ObservationsIntroduction Continuous data models Models for count data Models for categorical data Models for time-to-event data Joint models Modeling the Individual ParametersIntroduction Gaussian models Models with covariates Extensions to multivariate distributions Additional levels of variability Different mathematical representations and implementations of the same model ExtensionsMixture models Markov models Stochastic differential equation-based models Using ModelsTasks and MethodsIntroduction Estimation Model evaluation ExamplesBody weight curves in a toxicity study Joint PKPD modeling of warfarin data Gene expression in single cells AlgorithmsIntroduction The SAEM algorithm for estimating population parameters The Metropolis-Hastings algorithm for simulating the individual parameters Estimation of the observed Fisher information matrix Estimation of the log-likelihood Examples of calculating the log-likelihood and it derivatives Automatic construction of visual predictive checks AppendicesThe Individual ApproachSome Useful ResultsIntroduction to Pharmacokinetics ModelingTools Bibliography Glossary Index


Marc Lavielle is a statistician specializing in computational statistics and healthcare applications. He holds a Ph.D. in statistics from University Paris-Sud, Orsay. He was named professor at Paris Descartes University and joined Inria as research director in 2007. Creator of the Monolix software, he led the Monolix software development project at Inria between 2009 and 2011. He created the CNRS Research Group "Statistics and Health" in 2007. Since 2009, Dr. Lavielle has been a member of the French High Council of Biotechnologies, where he promotes the use of sound statistical methods to evaluate health and environmental risks related to genetically modified organisms (GMOs).

Altre Informazioni



Condizione: Nuovo
Collana: Chapman & Hall/CRC Biostatistics Series
Dimensioni: 9.25 x 6.125 in Ø 1.45 lb
Formato: Copertina rigida
Illustration Notes:147 b/w images and 15 tables
Pagine Arabe: 383

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