This text presents and describes methods for analysis of longitudinal data, with a strong emphasis on application of these methods to problems in the biomedical and behavioral sciences. Applied Longitudinal Data Analysis is geared more toward users, and not developers, of statistics.

Preface. Acknowledgments. Acronyms. 1. Introduction. 1.1 Advantages of Longitudinal Studies. 1.2 Challenges of Longitudinal Data Analysis. 1.3 Some General Notation. 1.4 Data Layout. 1.5 Analysis Considerations. 1.6 General Approaches. 1.7 The Simplest Longitudinal Analysis. 1.8 Summary. 2. ANOVA Approaches to Longitudinal Data. 2.1Single-Sample Repeated Measures ANOVA. 2.2 Multiple-Sample Repeated Measures ANOVA. 2.3 Illustration. 2.4 Summary. 3. MANOVA Approaches to Longitudinal Data. 3.1 Data Layout for ANOVA versus MANOVA. 3.2 MANOVA for Repeated Measurements. 3.3 MANOVA of Repeated Measures-s Sample Case. 3.4 Illustration. 3.5 Summary. 4. Mixed-Effects Regression Models for Continuous Outcomes. 4.1 Introduction. 4.2 A Simple Linear Regression Model. 4.3 Random Intercept MRM. 4.4 Random Intercept and Trend MRM.   4.5 Matrix Formulation. 4.6 Estimation . 4.7 Summary. 5. Mixed-Effects Polynomial Regression Models. 5.1 Introduction. 5.2 Curvilinear Trend Model. 5.3 Orthogonal Polynomials. 5.4 Summary. 6. Covariance Pattern Models. 6.1 Introduction. 6.2 Covariance Pattern Models. 6.3 Model Selection. 6.4 Example. 6.5 Summary. 7. Mixed Regression Models with Autocorrelated Errors. 7.1 Introduction. 7.2 MRMs with AC Errors. 7.3 Model Selection. 7.4 Example. 7.5 Summary. 8. Generalized Estimating Equations (GEE) Models. 8.1 Introduction. 8.2 Generalized Linear Models (GLMs). 8.3 Generalized Estimating Equations (GEE) Models. 8.4 GEE Estimation. 8.5 Example. 8.6 Summary. 9. Mixed-Effects Regression Models for Binary Outcomes. 9.1 Introduction. 9.2 Logistic Regression Model. 9.3 Probit Regression Models. 9.4 Threshold Concept. 9.5 Mixed-Effects Logistic Regression Model. 9.6 Estimation. 9.7 Illustration. 9.8 Summary. 10. Mixed-Effects Regression Models for Ordinal Outcomes. 10.1 Introduction. 10.2 Mixed-Effects Proportional Odds Model. 10.3 Psychiatric Example. 10.4 Health Services Research Example. 10.5 Summary. 11. Mixed-Effects Regression Models for Nominal Data. 11.1 Mixed-Effects Multinomial Regression Model. 11.2 Health Services Research Example. 1 1.3 Competing Risk Survival Models. 11.4 Summary. 12. Mixed-effects Regression Models for Counts. 12.1 Poisson Regression Model. 12.2 Modified Poisson Models. 12.3 The ZIP Model. 12.4 Mixed-Effects Models for Counts. 12.5 Illustration. 12.6 Summary. 13. Mixed-Effects Regression Models for Three-Level Data. 13.1 Three-Level Mixed-Effects Linear Regression Model. 13.1.1 Illustration. 13.2 Three-Level Mixed-Effects Nonlinear Regression Models. 13.3 Summary. 14. Missing Data in Longitudinal Studies. 14.1 Introduction. 14.2 Missing Data Mechanisms. 14.3 Models and Missing Data Mechanisms. 14.4 Testing MCAR. 14.5 Models for Nonignorable Missingness. 14.6 Summary. Bibliography. Topic Index.

Longitudinal data analysis for biomedical and behavioral sciences This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data. Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include: Repeated measures analysis of varianceMultivariate analysis of variance for repeated measuresRandom-effects regression models (RRM)Covariance-pattern modelsGeneralized-estimating equations (GEE) modelsGeneralizations of RRM and GEE for categorical outcomes Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge. This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference.

"…a useful resource for students and mathematically inclined practitioners…I would not hesitate to recommend this book…" (Journal of the American Statistical Association, September 2007) "Comparing this book with…other books on this subject…this is certainly one of the foremost." (Journal of Biopharmaceutical Statistics, Vol. 17, Issue 3, 2007) "...this book represents a unique and important contribution to the field of psychology." (PsycCRITIQUES, March 14, 2007) "This innovative classroom-tested book is…highly recommended." (CHOICE, October 2006)