» Applied Longitudinal Analysis (Wiley Series in Probability and Statistics)

Applied Longitudinal Analysis (Wiley Series in Probability and Statistics) Reviews

Customer Rating:
Summary: great addition to the literture on longitudinal data analysis
Comment: While the text by Diggle, Heagerty, Liang and Zeger now in its second edition is the first and in my opinion still the best book to cover the theory and methods of longitudinal data analysis, the subject has such great importance in biostatistics and clinical trial research that a number of excellent competitors have now come. This text is certainly one.

Nan Laird and James Ware are Harvard Professors of Biostatistics with a great deal of experience studying and publishing research on longitudinal data. Along with Fitzmaurice they have put together a book that provides a strong foundation in the methodology and a wealth of applications based on their experience.

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Customer Rating:
Summary: Quite Useful
Comment: This textbook is an excellent introduction or review of methods for analysis of longitudinal data, for applied researchers with all levels of statistical background.

Strengths
* Intuitive explanations for technical details
* Practical hints for approaching analysis
* Guidance on how to interpret results
* Good graphics and SAS tips

Weaknesses
* It's fine to gloss over technical details, but it would be useful to add a reference for where the details may be found. (Singer & Willett's Applied Longitudinal Data Analysis does this very well.) A glaring example is the relationship between marginal mean parameters and GLMM mean parameters in logistic regression with a random intercept (p. 363)
* Naturally the authors emphasize their own contributions to the field, but other approaches are either ignored (Bayesian solutions to likelihood-based models) or obliquely insulted without direct attribution (see the swipe at marginalized models on p. 364).
* Examples use only SAS sofware. The Singer & Willett ALDA website hosted by UCLA shows code for SAS, R, Stata, MPlus, MLwin, SPSS, and HLM!

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Customer Rating:
Summary: My favorite introductory statistics book
Comment: The authors have done a masterful job. They've created a book that is accessable to those without a strong mathematics background, but still interesting to those with such a background. The scope is broad, yet one does not feel "shortchanged" on any topic covered. They cover both linear and generalized linear models, with and without mixed effects. Part IV contains what the authors call advanced topics such as missing data and multilevel models and their lucidity, given such brief treatment is astonishing.

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Customer Rating:
Summary: It's very good introduction textbook
Comment: Tis book is very easy to read and understand. If you have the basic idea about the linear algebra. I recommend this book for people who want to self-teach. Since you can catch the concepts quickly.

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Customer Rating:
Summary: Perfect for the applied researcher
Comment: If you need to do longitudinal analyses, and have a moderate mathermatical background, this is a book you should get, particularly if you use SAS. The authors present a wide variety of models clearly, describe their advantages and disadvantages, and illustrate how to use SAS to fit them. They keep the technical level modest (a little use of matrix algebra, but no calculus; not in theorem-proof style) while not sacrificing needed detail. In addition, they provide, at the end of each chapter, two sets of references: One at a similar level to this book, and one with more advanced material for those who wish (and are able) to explore it.

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