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Binding : Broschiert, Label : Indel, Publisher : Indel, medium : Broschiert, numberOfPages : 205, publicationDate : 2003-03-14, authors : Konstantin Meyl, languages : german, ISBN : 3980254275

Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain improperly analyzed. This book teaches the art and statistical science of modern longitudinal data analysis. The author emphasizes specifying, understanding, and interpreting longitudinal data models. He inspects the longitudinal data graphically, analyzes the time trend and covariates, models the covariance matrix, and then draws conclusions. Covariance models covered include random effects, autoregressive, autoregressive moving average, antedependence, factor analytic, and completely unstructured models among others. Longer expositions explore: an introduction to and critique of simple non-longitudinal analyses of longitudinal data, missing data concepts, diagnostics, and simultaneous modeling of two longitudinal variables. Applications and issues for random effects models cover estimation, shrinkage, clustered data, models for binary and count data and residuals and residual plots. Shorter sections include a general discussion of how computational algorithms work, handling transformed data, and basic design issues. This book requires a solid regression course as background and is particularly intended for the final year of a Biostatistics or Statistics Masters degree curriculum. The mathematical prerequisite is generally low, mainly assuming familiarity with regression analysis in matrix form. Doctoral students in Biostatistics or Statistics, applied researchers and quantitative doctoral students in disciplines such as Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education will find this book invaluable. The book has many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce thematerial. From the reviews: "...This book is extremely well presented and it has been written in a style that makes its reading really pleasant and enjoyable...I highly recommend Modeling Longitudinal Data as a good reference book for anyone interested in looking into the art and statistical science of modern longitudinal data analysis." Journal of Applied Statistics, December 2005 "The book is clearly written and well presented. The author's accumulated experience in presenting the material comes over. On balance, this is one of the books which anyone about to teach a practical course in longitudinal data analysis should consider adopting as the course text." Short Book Reviews of the ISI, June 2006 "...Modeling Longitudinal Data is a welcome addition to the vast literature on longitudinal data analysis. The book requires little in terms of prerequisites but offers a great deal." Zhigang Zhang for the Journal of the American Statistical Association, December 2006 "Overall, Robert Weiss's book can be...

Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain improperly analyzed. This book teaches the art and statistical science of modern longitudinal data analysis. The author emphasizes specifying, understanding, and interpreting longitudinal data models. He inspects the longitudinal data graphically, analyzes the time trend and covariates, models the covariance matrix, and then draws conclusions. Covariance models covered include random effects, autoregressive, autoregressive moving average, antedependence, factor analytic, and completely unstructured models among others. Longer expositions explore: an introduction to and critique of simple non-longitudinal analyses of longitudinal data, missing data concepts, diagnostics, and simultaneous modeling of two longitudinal variables. Applications and issues for random effects models cover estimation, shrinkage, clustered data, models for binary and count data and residuals and residual plots. Shorter sections include a general discussion of how computational algorithms work, handling transformed data, and basic design issues. This book requires a solid regression course as background and is particularly intended for the final year of a Biostatistics or Statistics Masters degree curriculum. The mathematical prerequisite is generally low, mainly assuming familiarity with regression analysis in matrix form. Doctoral students in Biostatistics or Statistics, applied researchers and quantitative doctoral students in disciplines such as Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education will find this book invaluable. The book has many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce thematerial. From the reviews: "...This book is extremely well presented and it has been written in a style that makes its reading really pleasant and enjoyable...I highly recommend Modeling Longitudinal Data as a good reference book for anyone interested in looking into the art and statistical science of modern longitudinal data analysis." Journal of Applied Statistics, December 2005 "The book is clearly written and well presented. The author's accumulated experience in presenting the material comes over. On balance, this is one of the books which anyone about to teach a practical course in longitudinal data analysis should consider adopting as the course text." Short Book Reviews of the ISI, June 2006 "...Modeling Longitudinal Data is a welcome addition to the vast literature on longitudinal data analysis. The book requires little in terms of prerequisites but offers a great deal." Zhigang Zhang for the Journal of the American Statistical Association, December 2006 "Overall, Robert Weiss's book can be...

Psychological and educational researchers in the Scandinavian countries have cooperated in a research effort relating to children's learning disabilities for more than a decade. Support has come from the federal governments and other funding agencies in Norway, Sweden, and Denmark through the Secretariat for Scan­ dinavian Cultural Cooperation. A number of independent studies have already been published, dealing with various aspects oflearning disabilities in the literacy skills of reading and writing. The largest and most comprehensive study was the Bergen Project, a longitudi­ nal study of an entire cohort of children, with special emphasis on those who developed specific learning disabilities in reading and writing (dyslexia). These dyslexic children were studied, diagnosed, and treated over a period of nine years, along with various control and comparison groups, which included a large subgroup with general learning disabilities (retarded). The Bergen Project involved the collection of voluminous data. The children were identified by means of special diagnostic tests and treated using remedial materials and techniques that had been developed to deal with various types of dyslexia. The ophthalmology team not only tested the children, but they also prescribed and provided glasses, and even performed surgery when necessary. The pediatric neurologists did general pediatric and neurological examinations, following up many of the cases with EEGs and CT (computerized tomography, brain x-rays).

This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for AppliedStatistics (2001–2004) and as Associate Editor for several journals, including Biometrics and Biostatistics. He was President of the International Biometric Society (2004–2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Geert Verbeke is Professor of Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He has published a number of methodological articles on various aspects of models for longitudinal data analyses, with particular emphasis on mixed models. Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), and Joint Editor of the Journal of the Royal Statistical Society, Series A (2005–2008). He has served as Associate Editor for several journals including Biometrics and Applied Statistics. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer,...

The SAS routines on mixed models have applications in many areas of statistics, especially biostatistics, but the procedures are not well- documented. Based on short courses given by the authors, this book provides practical guidance for SAS users.

This paperback edition is a reprint of the 2000 edition. 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 mixed 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. However, some other commercially available packages are discussed as well. Great care has been taken in presenting the data analyses in a software-independent fashion.

The SAS routines on mixed models have applications in many areas of statistics, especially biostatistics, but the procedures are not well- documented. Based on short courses given by the authors, this book provides practical guidance for SAS users.

This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for AppliedStatistics (2001–2004) and as Associate Editor for several journals, including Biometrics and Biostatistics. He was President of the International Biometric Society (2004–2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Geert Verbeke is Professor of Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He has published a number of methodological articles on various aspects of models for longitudinal data analyses, with particular emphasis on mixed models. Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), and Joint Editor of the Journal of the Royal Statistical Society, Series A (2005–2008). He has served as Associate Editor for several journals including Biometrics and Applied Statistics. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer,...

This volume is the product of a course on longitudinal prospective re­ search arranged by the three editors in Arhus, Denmark, in 1978. The course was supported by the Nordisk Kulturfond for young researchers from the Nordic countries, who had planned or had simply involved themselves in longitudinal prospective research projects of various kinds. The twenty-six participants represented a wide range of professions: statisticians, psychologists, psychiatrists, nutritionists, and public health researchers. The teachers came from many countries and represented many disciplines. The course was very successful, especially from the point of view of the quality and investment of the teachers. We felt also that the course met a strong need in this relatively new field of research. Therefore, we asked the teachers to prepare written versions of their lectures so that they could have wider dissemination; they agreed to do so. The present book is composed of these contributions. The first chap­ ter, after outlining some of the problems with traditional strategies in mental health research, goes on to suggest some of the possible preven­ tive applications of longitudinal research methods. Included in Parts II and III are papers on design problems and on the tools of long-term research, such as genetics and classification, biological measurements, epidemiological guidelines, statistical models, disease registers, and de­ velopmental psychology.

Criminology is concerned with many questions which are inherently longitudinal. For example, what is the life-course of criminal behavior? Is there one general offending pattern or multiple offending patterns over time? Which early risk factors, if any, are strongly predictive of criminal behavior? Can early intervention prevent the development of a criminal career? Longitudinal research allows examination of within-person relationships over time, and allows the examination of developmental sequences and timing. This volume examines longitudinal research in relation to crime and delinquency. The main body of Longitudinal Studies on Crime and Delinquency is seven reviews, which were commissioned to answer two simultaneous questions: What have we learned from recent longitudinal research on crime and delinquency that (a) we did not know before, and (b) that capitalizes on the longitudinal nature of the data? Topics for review were chosen with an eye to three considerations: (a) a critical mass of studies addresses the question; (b) an emphasis on longitudinal methods; (c) policy relevance of the question. Three additional chapters include an introduction and overview, an essay reflecting on the findings highlighted in the volume from the broad perspective of the evolutionary ecological theory of crime, and a Future Directions chapter. The volume also includes an appendix which relates each of the reviews to the body of longitudinal studies reviewed in the volume. This not only shows which studies have informed which topics, but also highlights analytic opportunities that have not yet been explored and where this information could be applied in future research.

Criminology is concerned with many questions which are inherently longitudinal. For example, what is the life-course of criminal behavior? Is there one general offending pattern or multiple offending patterns over time? Which early risk factors, if any, are strongly predictive of criminal behavior? Can early intervention prevent the development of a criminal career? Longitudinal research allows examination of within-person relationships over time, and allows the examination of developmental sequences and timing. This volume examines longitudinal research in relation to crime and delinquency. The main body of Longitudinal Studies on Crime and Delinquency is seven reviews, which were commissioned to answer two simultaneous questions: What have we learned from recent longitudinal research on crime and delinquency that (a) we did not know before, and (b) that capitalizes on the longitudinal nature of the data? Topics for review were chosen with an eye to three considerations: (a) a critical mass of studies addresses the question; (b) an emphasis on longitudinal methods; (c) policy relevance of the question. Three additional chapters include an introduction and overview, an essay reflecting on the findings highlighted in the volume from the broad perspective of the evolutionary ecological theory of crime, and a Future Directions chapter. The volume also includes an appendix which relates each of the reviews to the body of longitudinal studies reviewed in the volume. This not only shows which studies have informed which topics, but also highlights analytic opportunities that have not yet been explored and where this information could be applied in future research.

Criminology is concerned with many questions which are inherently longitudinal. For example, what is the life-course of criminal behavior? Is there one general offending pattern or multiple offending patterns over time? Which early risk factors, if any, are strongly predictive of criminal behavior? Can early intervention prevent the development of a criminal career? Longitudinal research allows examination of within-person relationships over time, and allows the examination of developmental sequences and timing. This volume examines longitudinal research in relation to crime and delinquency. The main body of Longitudinal Studies on Crime and Delinquency is seven reviews, which were commissioned to answer two simultaneous questions: What have we learned from recent longitudinal research on crime and delinquency that (a) we did not know before, and (b) that capitalizes on the longitudinal nature of the data? Topics for review were chosen with an eye to three considerations: (a) a critical mass of studies addresses the question; (b) an emphasis on longitudinal methods; (c) policy relevance of the question. Three additional chapters include an introduction and overview, an essay reflecting on the findings highlighted in the volume from the broad perspective of the evolutionary ecological theory of crime, and a Future Directions chapter. The volume also includes an appendix which relates each of the reviews to the body of longitudinal studies reviewed in the volume. This not only shows which studies have informed which topics, but also highlights analytic opportunities that have not yet been explored and where this information could be applied in future research.

Criminology is concerned with many questions which are inherently longitudinal. For example, what is the life-course of criminal behavior? Is there one general offending pattern or multiple offending patterns over time? Which early risk factors, if any, are strongly predictive of criminal behavior? Can early intervention prevent the development of a criminal career? Longitudinal research allows examination of within-person relationships over time, and allows the examination of developmental sequences and timing. This volume examines longitudinal research in relation to crime and delinquency. The main body of Longitudinal Studies on Crime and Delinquency is seven reviews, which were commissioned to answer two simultaneous questions: What have we learned from recent longitudinal research on crime and delinquency that (a) we did not know before, and (b) that capitalizes on the longitudinal nature of the data? Topics for review were chosen with an eye to three considerations: (a) a critical mass of studies addresses the question; (b) an emphasis on longitudinal methods; (c) policy relevance of the question. Three additional chapters include an introduction and overview, an essay reflecting on the findings highlighted in the volume from the broad perspective of the evolutionary ecological theory of crime, and a Future Directions chapter. The volume also includes an appendix which relates each of the reviews to the body of longitudinal studies reviewed in the volume. This not only shows which studies have informed which topics, but also highlights analytic opportunities that have not yet been explored and where this information could be applied in future research.

This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effects model is changed into a dynamic one by the introduction of the auto-regression term. Response levels in this model gradually move toward an asymptote or equilibrium which depends on covariates and random effects. The book provides relationships of the autoregressive linear mixed effects models with linear mixed effects models, marginal models, transition models, nonlinear mixed effects models, growth curves, differential equations, and state space representation. State space representation with a modified Kalman filter provides log likelihoods for maximum likelihood estimation, and this representation is suitable for unequally spaced longitudinal data. The extension to multivariate longitudinal data analysis is also provided. Topics in medical fields, such as response-dependent dose modifications, response-dependent dropouts, and randomized controlled trials are discussed. The text is written in plain terms understandable for researchers in other disciplines such as econometrics, sociology, and ecology for the progress of interdisciplinary research.

This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches. The book is technically rigorous, and, it also presents illustrations of the statistical analysis of various real life data involving univariate multinomial responses both in cross-sectional and longitudinal setups. This book is written mainly for the graduate students and researchers in statistics and social sciences, among other applied statistics research areas. However, the rest of the book, specifically the chapters from 1 to 3, may also be used for a senior undergraduate course in statistics.

This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effects model is changed into a dynamic one by the introduction of the auto-regression term. Response levels in this model gradually move toward an asymptote or equilibrium which depends on covariates and random effects. The book provides relationships of the autoregressive linear mixed effects models with linear mixed effects models, marginal models, transition models, nonlinear mixed effects models, growth curves, differential equations, and state space representation. State space representation with a modified Kalman filter provides log likelihoods for maximum likelihood estimation, and this representation is suitable for unequally spaced longitudinal data. The extension to multivariate longitudinal data analysis is also provided. Topics in medical fields, such as response-dependent dose modifications, response-dependent dropouts, and randomized controlled trials are discussed. The text is written in plain terms understandable for researchers in other disciplines such as econometrics, sociology, and ecology for the progress of interdisciplinary research.

This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches. The book is technically rigorous, and, it also presents illustrations of the statistical analysis of various real life data involving univariate multinomial responses both in cross-sectional and longitudinal setups. This book is written mainly for the graduate students and researchers in statistics and social sciences, among other applied statistics research areas. However, the rest of the book, specifically the chapters from 1 to 3, may also be used for a senior undergraduate course in statistics.

This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches. The book is technically rigorous, and, it also presents illustrations of the statistical analysis of various real life data involving univariate multinomial responses both in cross-sectional and longitudinal setups. This book is written mainly for the graduate students and researchers in statistics and social sciences, among other applied statistics research areas. However, the rest of the book, specifically the chapters from 1 to 3, may also be used for a senior undergraduate course in statistics.

This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalized estimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up. The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents the illustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University ResearchProfessor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to the society including his special services for society’s annual meetings.