Linear Mixed Models for Longitudinal DataSpringer Science & Business Media, 2009 M04 28 - 570 páginas This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, conditional linear mid models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. How3ever, some other commercially available packages are discussed as well. Great care has been taken in presenting the data analyses in a software-independent fashion. Geert Verbeke is Assistant Professor at the Biostistical Centre of the Katholieke Universiteit Leuven in Belgium. He received the B.S. degree in mathematics (1989) from the Katholieke Universiteit Leuven, the M.S. in biostatistics (1992) from the Limburgs Universitair Centrum, and earned a Ph.D. in biostatistics (1995) from the Katholieke Universiteit Leuven. Dr. Verbeke wrote his dissertation, as well as a number of methodological articles, on various aspects of linear mixed models for longitudinal data analysis. He has held visiting positions at the Gerontology Research Center and the Johns Hopkins University. Geert Molenberghs is Assistant Professor of Biostatistics at the Limburgs Universitair Centrum in Belgium. He received the B.S. degree in mathematics (1988) and a Ph.D. in biostatistics (1993) from the Universiteit Antwerpen. Dr. Molenberghs published methodological work on the analysis of non-response in clinical and epidemiological studies. He serves as an associate editor for Biometrics, Applied Statistics, and Biostatistics, and is an officer of the Belgian Statistical Society. He has held visiting positions at the Harvard School of Public Health. |
Contenido
Introduction | 1 |
A Model for Longitudinal Data | 3 |
299 | 19 |
Exploratory Data Analysis | 54 |
Inference for the Marginal Model | 55 |
Inference for the Random Effects | 77 |
Fitting Linear Mixed Models with | 93 |
General Guidelines for Model Building | 121 |
Conditional Linear Mixed Models 189 | 188 |
Exploring Incomplete Data | 201 |
Joint Modeling of Measurements and Missingness | 209 |
Simple Missing Data Methods 221 | 220 |
PatternMixture Models | 274 |
Sensitivity Analysis for Selection Models | 295 |
How Ignorable Is Missing At Random ? | 375 |
Case Studies | 405 |
Exploring Serial Correlation | 135 |
Local Influence for the Linear Mixed Model | 151 |
The Heterogeneity Model | 169 |
Appendix | 485 |
Otras ediciones - Ver todas
Linear Mixed Models for Longitudinal Data Geert Verbeke,Geert Molenberghs Vista previa limitada - 2008 |
Linear Mixed Models for Longitudinal Data Geert Verbeke,Geert Molenberghs Vista previa limitada - 2009 |
Linear Mixed Models for Longitudinal Data Geert Verbeke,Geert Molenberghs Vista previa limitada - 2001 |
Términos y frases comunes
algorithm analysis assumed assumption baseline cancer conditional linear mixed corresponding covariance matrix covariance structure data set degrees of freedom Diggle and Kenward dropout model dropout pattern EB estimates EM algorithm endpoint example F-test Figure fitted fixed effects fixed-effects Growth Data imputation inference Lesaffre likelihood function likelihood ratio likelihood ratio test linear mixed model log-likelihood longitudinal data marginal model maximal maximum likelihood estimation MCAR mean structure measurement error Megestrol Acetate method missing data missing values missingness MNAR Molenberghs normally distributed obtained option p-value parameter estimates pattern-mixture models PROC MIXED prostate cancer Prostate Data quadratic time effect random effects random intercepts random slopes random-effects model Rat Data REML estimates residual sample selection model serial correlation specified standard errors statistic subject-specific surrogate endpoint Table time2 treatment effect variability variance components variance function vector Verbeke Vorozole Vorozole Study Wald test