Longitudinal Data Analysis by Donald Hedeker (English) Hardcover Book

Description: Longitudinal Data Analysis by Donald Hedeker, Robert D. Gibbons 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. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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 variance * Multivariate analysis of variance for repeated measures * Random-effects regression models (RRM) * Covariance-pattern models * Generalized-estimating equations (GEE) models * Generalizations 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 titles 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. Back Cover 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 variance Multivariate analysis of variance for repeated measures Random-effects regression models (RRM) Covariance-pattern models Generalized-estimating equations (GEE) models Generalizations 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 titles 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. Flap 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 variance Multivariate analysis of variance for repeated measures Random-effects regression models (RRM) Covariance-pattern models Generalized-estimating equations (GEE) models Generalizations 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 titles 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. Author Biography DONALD HEDEKER, PHD, is Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health at the University of Illinois at Chicago. He is a Fellow of the American Statistical Association and the author of numerous peer-reviewed papers. ROBERT D. GIBBONS, PHD, is Director of the Center for Health Statistics; Professor of Biostatistics in the Division of Epidemiology and Biostatistics, School of Public Health; and Professor of Psychiatry in the College of Medicine, all at the University of Illinois at Chicago. He is a Fellow of the American Statistical Association and the author of numerous peer-reviewed papers. Table of Contents 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. Review "…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) Long Description 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 variance Multivariate analysis of variance for repeated measures Random-effects regression models (RRM) Covariance-pattern models Generalized-estimating equations (GEE) models Generalizations 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 titles 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. Review Text "?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) Review Quote "This innovative classroom-tested book is... highly recommended." ("CHOICE," October 2006) Promotional "Headline" "...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) Feature An important book on longitudinal data analysis. Includes exercises that can be used for self-testing. Focuses on detailed explanations of the various statistical models and their parameters. Contains generalizations of RRM and GEE for categorical outcomes. Details ISBN0471420271 Author Robert D. Gibbons Short Title LONGITUDINAL DATA ANALYSIS Series Wiley Series in Probability and Statistics Language English ISBN-10 0471420271 ISBN-13 9780471420279 Media Book Format Hardcover Year 2006 Place of Publication New York Country of Publication United States Alternative 9780470036488 Edition 1st Affiliation University of Illinois at Chicago DOI 10.1604/9780471420279 Series Number 451 UK Release Date 2006-05-09 NZ Release Date 2006-04-01 Pages 368 Publisher John Wiley & Sons Inc Publication Date 2006-05-09 Imprint Wiley-Interscience DEWEY 519.5 Illustrations Graphs: 76 B&W, 0 Color Audience Professional & Vocational US Release Date 2006-05-09 AU Release Date 2006-03-20 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:2555624;

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