Linear Mixed Models for Longitudinal DataSpringer Science & Business Media, 12 mai 2009 - 570 pages 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. |
Table des matières
1 | |
A Model for Longitudinal Data | 19 |
Exploratory Data Analysis | 31 |
Estimation of the Marginal Model | 41 |
Inference for the Marginal Model | 55 |
Inference for the Random Effects | 77 |
Fitting Linear Mixed Models with SAS | 93 |
General Guidelines for Model Building | 121 |
Simple Missing Data Methods | 221 |
Selection Models 231 | 230 |
PatternMixture Models | 275 |
Sensitivity Analysis for Selection Models 295 | 294 |
Sensitivity Analysis for PatternMixture Models | 331 |
How Ignorable Is Missing At Random ? | 375 |
The ExpectationMaximization Algorithm | 387 |
Case Studies | 405 |
Exploring Serial Correlation | 135 |
Local Influence for the Linear Mixed Model | 151 |
The Heterogeneity Model 169 | 168 |
Conditional Linear Mixed Models | 189 |
Exploring Incomplete Data | 201 |
Joint Modeling of Measurements and Missingness 209 | 208 |
Appendix | 485 |
B Technical Details for Sensitivity Analysis | 515 |
References 523 | 522 |
554 | |
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Expressions et termes fréquents
ACMV algorithm assumed assumption cancer corresponding covariance matrix covariance structure data set degrees of freedom Diggle and Kenward dropout model dropout pattern dropout process EB estimates equal example F-test Figure fitted fixed effects fixed-effects Growth Data imputation incomplete inference influence Laird Lesaffre likelihood function likelihood ratio likelihood ratio test linear mixed model log-likelihood longitudinal data marginal model mastitis maximal maximum likelihood estimates MCAR mean structure measurement error Megestrol Acetate method missing data missing value missingness MNAR Molenberghs multivariate nonrandom dropout normally distributed obtained outcomes p-value parameter estimates pattern-mixture models plots predicted PROC MIXED prostate cancer Prostate Data random effects random intercepts random slopes random-effects model Rat Data REML estimates residual restrictions sample selection model serial correlation shown specified standard errors statement strategies subject-specific surrogate endpoint Table tion treatment effect trials variability variance components variogram vector Verbeke Vorozole Vorozole Study Wald test