This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
Peter Diggle, Department of Mathematics and Statistics, University of Lancaster Patrick Heagerty, Biostatistics department University of Washington Kung-Yee Liang, Biostatistics department, Johns Hopkins University Scott Zeger, Biostatistics department, Johns Hopkins University
Inhaltsangabe
1: Introduction 2: Design considerations 3: Exploring longitudinal data 4: General linear models 5: Parametric models for covariance structure 6: Analysis of variance methods 7: Generalized linear models for longitudinal data 8: Marginal models 9: Random effects models 10: Transition models 11: Likelihood-based methods for categorical data 12: Time-dependent covariates 13: Missing values in longitudinal data 14: Additional topics Appendix Bibliography Index
1: Introduction 2: Design considerations 3: Exploring longitudinal data 4: General linear models 5: Parametric models for covariance structure 6: Analysis of variance methods 7: Generalized linear models for longitudinal data 8: Marginal models 9: Random effects models 10: Transition models 11: Likelihood-based methods for categorical data 12: Time-dependent covariates 13: Missing values in longitudinal data 14: Additional topics Appendix Bibliography Index
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