Book Description
Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: BL Downloadable data sets BL Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more BL Additional material for data analysis
Customer Reviews:
A Wonderful Work.......2007-07-15
I find Professor Singer's Book to be a most informative and useful tool for anyone who wishes to better understand Multilevel Modeling.
Applied Longitudinal Data Analysis by Singer,et al.......2007-03-13
Clearly written text... and usefull for researchers.
I would recommend it to anyone starting to learn about the subject!
The Clearest and Most Useful Book on HLM for Longitudinal Studies.......2006-07-28
This is simply the best book for those analyzing longitudinal data (data measured at more than one time point). Singer's coverage of Hierarchical Linear MOdeling (HLM) is clear, well-written (sprinkled with humor, it's like a lecture by the most popular prof. at your school), and geared towards researchers who need their programs to run, not just learn the mathematical underpinnings. Singer and Willett (the coauthor, not listed above!) set the standard for presenting math/statistics book examples.
THe authors accomplish the latter by keying her examples to data located at a UCLA website; you can run the same programs on the same datasets used in the book (wow!), and compare your output, troubleshooting any problems you may have. Singer and Willett (her coauthor, not listed here!) provide outputs and programs correspoing to several of the most popular statistical programs, including SAS and SPSS.
SInger and Willet also explain the rationale for using HLM over more traditional techniques such as regression. Simply stated, regression aggregates at a level that cause one to lose information (and hence the power to detect differences.) HLM allows one to look at overall differences due to time, but also the trajectories of individual differences who are "nested" within those time points. It's the (relatively) new thing, and is increasing used by investigators, and desired by peer reviewers.
As supplements, I suggest using the UCLA website mentioned above, subscribing to an e-mail LISTSERV for interesting (though sometimes compicated discussions of "multilevel modeling" (MULTILEVEL@JISCMAIL.AC.UK), and searching for Judith Singer's website through Google or A9 (if you use A9--"Alexa"--enough you'll get a small discount at Amazon.com). Also, compare Amazon's and Judith Singer's (through her website) current prices on this book.
Breaking down complex analyses .......2006-03-18
This is an excellent book. Multilevel modeling and survival analysis are becoming increasingly important in psychological studies, but are pretty complicated procedures. Singer & Willet offer both a conceptual background and practical ways to do the analyses in a clear, understandable manner. The book is very readable and will be an important reference for future analyses!
very clear and thorough.......2006-03-16
This book does a particularly good job of explaining the substantive meaning of the equations involved in multilevel modeling analyses. It spends a lot of extra time explaining what the equations mean in real world terms using examples from actual data sets. I teach a graduate level course on HLM and I much prefer this book to the Raudenbush & Byrk book because it not only does a better job of explaining the math (for graduate students less comfortable with statistics) but the chapters are also sprinkled with incredibly useful advice on actually running the analyses (getting them to converge, interpreting them, etc.) The Raudenbush & Bryk book probably does a slightly better job of presenting the equations, but it falls short on explanation and practical advice. If you were only going to buy one HLM book, I would start with this one.
Book Description
The most important techniques available for longitudinal data analysis are discussed in this book. The discussion includes simple techniques such as the paired t-test and summary statistics, but also more sophisticated techniques such as generalized estimating equations and random coefficient analysis. A distinction is made between longitudinal analysis with continuous, dichotomous, and categorical outcome variables. This practical guide is especially suitable for non-statisticians and all those undertaking medical research or epidemiological studies.
Customer Reviews:
GREAT book! .......2004-12-15
This book is really useful and handy. It is very well written and easy to read. As the name stated, it provides very practical guides for those who don't have strong background in Statistics but are dealing with longitudinal data. It is written in an example guided format. The outputs from the analysis and guidelines on how to interpret them step by step are included. There is no heavy Statistical notation and you don't need to translate Statistics into English. At the end of the book, there are chapters of how to handle missing data and softwares used in longitudinal data analysis. This book is probably too boring if you are a hardcore Statistician.
Book Description
Focusing on an analysis of models and data that arise from repeated observations of a cross-section of individuals, households or firms, this book also covers important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented social science graduate students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also demonstrates substantive applications from across the social sciences. These applications are enhanced by real-world data sets and software programs in SAS and Stata.
Customer Reviews:
Very comprehensive book.......2006-11-15
This book is an excelent complement to panel data textbooks (such as Arellano's and Balthagi's) because it deals with problems generally encountered in microdata, such as sample selection.
This is an EXCELLENT book on panel data analysis!!.......2006-09-15
This is a very good book on panel data analysis. The author nicely summarized the key ideas about panel data from a few academic fields (biostatistics, econometrics, social science, and general statistics). The analysis of panel data could be confusing because different people in different fields could call the same thing by different names. People in econometrics sometimes ignore what is well developed in biostatistics, and vice versa. The author built the bridge among all these fields using an unified language.
This is a book about applied statistics. Theories and mathematics are covered in the appendices at the end of each chapter and the end of the book. A lot of examples from economics and social science are demonstrated in the book. Exercises are well organized and used to provide additional insights.
Highly recommend for applied statisticians.
Average customer rating:
- they were the first and they are still one of the best
- the long awaited second edition
- already the classic book on longitudinal data analysis
- Excellent, highly recommended!
|
Analysis of Longitudinal Data
Peter Diggle ,
Patrick Heagerty ,
Kung-Yee Liang , and
Scott Zeger
Manufacturer: Oxford University Press, USA
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Similar Items:
-
Applied Longitudinal Analysis (Wiley Series in Probability and Statistics)
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Linear Mixed Models for Longitudinal Data
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SAS for Mixed Models, Second Edition
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Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
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Statistical Analysis with Missing Data, Second Edition
ASIN: 0198524846 |
Book Description
The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This new edition of Analysis for Longitudinal Data provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.
Customer Reviews:
they were the first and they are still one of the best.......2007-08-18
The first edition of this book was a major success as for the first time advanced methods for the use of longitudinal data were introduced. Longitudinal data (sometimes also referred to as repeated measures data) is very important in the analysis of clinical trial data. This is because many important trial endpoints are collected for each patient at several visits over the course of the trial and the study sponsor (usually the manufacturer of a drug or a device)will want to see how the measures change over time with usually the baseline measurement and the last measurement being the most important. Often they want to see in a randomized trial whether the treatment over inerest tends to perform better for the subjects taking the test treatment versus those who take the active control and/or placebo. An issue is the presence of correlation between measurements from one time point to another.
So this type of analysis is similar to time series analysis. The difference is that time series are usually studied in the situation where a single series is observed for a long time and the analyst wants to determine future behavior based on an model constructed to fit this one observed series very well. The model is intended in the time series setting to describe a stochastic process (usually a stationary process or one transformed to stationarity by removal of trends). On the other hand in longitudinal analysis each patients profile over time is usually a very short series and the collection of these series over several patients in a particular treatment group are view to come from the same stochastic process. So the data represent several short partial realizations of the stochastic process while a time series is a long, single partial realization.
Since the data differ the methods of analyses differ also. For time seies analysis the autoregressive integrated moving average models of Box and Jenkins are often employed while for longitudinal data the mixed effect linear models are often the class of models chosen. The common theme is the structure of the covariance matrix for the observations in time series and the model noise terms in the case of the linear mixed models.
Zeger and Liang were among the leaders in developing successful modelling for these data. In a series of articles they develop a restricted maximum likelihood approach to the problem of estimating the model parameters and introduce a method called GEE an acronym for generalized estimating equations. The first edition of this book was very popular in the statistical community, particularly for statisticians working in the pharmaceutical industry. Along with Peter Diggle these three authors presented in the first edition this research organized into a single book for the first time. Now there is a plethora of books some prinarily theoretical and others primarily applied. The issue of missing data is very common to this type of data particularly when the data come from a clinical trial. The research of Molenberghs and Verbeke, covered by them in some repeated measures books, has shown these models to be among the most useful for handling missing data in realistic ways.
This second edition of this book has even greater coverage of topics and includes a fourth author Patrick Heagerty. Each of the four authors are skill research statisticians who specialize in biostatistics and particularly longitudinal data. While today there are many books to choose, this text continues ot be among the best.
the long awaited second edition.......2002-08-22
The second edition is much like the first and is at least a year behind the original schedule. See my review of the first edition to understand why this is a classical. The promised advances in missing data are included and a new author Haegerty has been added. The missing data chapter is three times longer than in the first edition. They cover what they promised. They also mention some of the econometrics literature including the work of Nobel Laureate James Heckman but admit in the preface that they do not know that literature very well and hence do not cover it in depth.
In the past two years Verbeke and Molenberghs have produced a highly competitive book that deals in detail with pattern mixture models and other missing data methodology but curiously Diggle et al. do not reference it even though they do cite some of Molenberghs work.
already the classic book on longitudinal data analysis.......2000-07-25
When this book came out in 1994 there was a great need to look differently at clinical data on subjects. Typically such data would have repeated measurements over time for many subjects but for only a few time points (say three to five). Standard analysis of variance methods do not properly account for within patient correlation between measurements. Time series analysis generally is good for treating long series (but usually only one or a few). In the clinical setting we often are considering hundreds of patients over short time intervals. This book is clearly written for intermediate level statistics students.
The field is important and rapidly developing. Though slightly dated the book is still an excellent introduction to the subject and a very good reference. However, a second edition is in the works and should be out in about one year. I recently took a short course from the authors and I know that the second edition will have some nice features including the latest advances for dealing with missing data and ways to combined the information from time to event data with the repeated measures data. It may be that if longitudinal data analysis is important to you, read the first edition at your favorite university library and save your money for the second edition.
The book includes some nice treatment of the important but often neglected topic of sample size determination.
Excellent, highly recommended!.......1998-07-14
This book was written by three very prestigious authors, two of which work at The Johns Hopkins University(Dr. Liang and Dr. Zeger), and Dr. Diggle, who is working in England. These three are very well known and respected characters in their field of work, and this book is an excellent reflection upon the research and work they have done over the years. Watch out! the key word is: GEE
Book Description
Fixed Effects Regression Methods for Longitudinal Data Using SAS is an invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques. First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. Designed to eliminate major biases from regression models with multiple observations (usually longitudinal) for each subject (usually a person), fixed effects methods essentially offer control for all stable characteristics of the subjects, even characteristics that are difficult or impossible to measure. This straightforward and thorough text shows you how to estimate fixed effects models with several SAS procedures that are appropriate for different kinds of outcome variables. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. The book contains thorough discussions of the following uses of SAS procedures: PROC GLM for estimating fixed effects linear models for quantitative outcomes, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models for repeated event data, PROC GENMOD for estimating fixed effects Poisson regression models for count data, PROC CALIS for estimating fixed effects structural equation models. To gain the most benefit from this book, readers should be familiar with multiple linear regression, have practical experience using multiple regression on real data, and be comfortable interpreting the output from a regression analysis. An understanding of logistic regression and Poisson regression is a plus. Some experience with SAS is helpful, but not required.
Customer Reviews:
Fixed Effects Regression Methods for Longitudinal Data Using SAS .......2007-01-09
It came in quickly and we appreciate it.
Book 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 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.
Customer Reviews:
Nice Book.......2007-03-17
A good general book on Anova, Manova, Fixed and Random Effects longitudinal models.
Book Description
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 that the reader can skip the software-oriented chapters and sections without breaking the logical flow.
From the reviews:
"Strengths of this book include its breadth of topics, excellent organization and clarity of writing...I highly recommend this book to my colleagues and students." -
Justine Shults for the Journal of Biopharmaceutical Statistics, Issue 3, 2006
"
Models for Discrete Longitudinal Data is an excellent choice for any statistician with an interest in analyzing discrete longitudinal data. It covers all of the theoretical and applied aspects in this area and is organized in such a way to serve as a handy reference guide for applied statisticians, especially those in biomedical fields. I learned a great deal from this book, and I recommend it highly to others." -
John Williamson for the Journal of the American Statistical Association, September 2006
Book Description
During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time-dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.
Average customer rating:
|
Longitudinal Data Analysis (Paul F. Lazarsfeld lecture in sociology series)
James S. Coleman
Manufacturer: Basic Books
ProductGroup: Book
Binding: Hardcover
General
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| Nonfiction
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ASIN: 0465042244 |
Book Description
Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other "people" events.
Customer Reviews:
Sometimes, it is more complicated than a pre/post test.......2007-03-01
Often, if we want to examine the effect of some particular event on something we are measuring/observing, we often resort to a simple pre/post test for all of the wrong reasons: it is easy and/or we did not know any better. This short text is a discussion on performing regression analysis when longitudinal events are involved. This text is not a 'how to' book.
By 'discussion', I mean that this book really is a discussion. This short book is primarily text where the author uses words, not equations [less than one per page] to discuss events and things that we should consider when examining them using regression analysis. In the text, the author makes a very strong effort to remind us that "censoring" and time-varying explanatory variables are prone to severe bias and the loss of information.
The author does an nice job of explaing his points though the use of well-considered examples. The chapters of the book are:
1) Introduction
2) A Discrete-Time Method
3) Parametric Methods for Continuous-Time Data
4) Proportional Hazards and Partial Likelihood
5) Multiple Kinds of Events
6) Repeated Events
7) Change of States
8) Conclusion
At the end of the text, there are several appendicies and references listed. One appendix is a listing of computer routines that can be used in this analysis. Though they are rather old now, I did a web search and found that they are still in use.
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