Nonlinear Models for Repeated Measurement Data
Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.
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Nonlinear regression models for individual data
Hierarchical linear models
Hierarchical nonlinear models 07
Inference based on individual estimates
Inference based on linearization
Nonparametric and semiparametric inference
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additional algorithm allow analysis applications approach appropriate approximate assay associated assumed assumption Bayesian Chapter characterization components computational concentration conditional consider constant context correlation corresponding covariance covariance matrix density dependence derivatives described discussed distribution dose elements empirical Bayes equations error estimation example extension Figure fixed framework function further given growth hierarchical implementation individual inference interest intervals intra-individual issues iteration known least squares linear linear model marginal matrix maximum likelihood mean mean response measurements methods minimizing nonlinear nonlinear model normal Note observations obtained parameters particular pattern pharmacokinetic plot pooled population possible procedure profiles random effects referred regression regression parameters relationship relative represents residuals respectively response sample shows specification Stage standard step structure suggests Table taken techniques tion values variability variance variation vector weight