Average Reviews:
(More customer reviews)First of all, this isn't really a textbook. It's more of an encyclopedia-textbook hybrid. You won't find proofs of theorems or (many) fully worked examples, however you'll get thorough descriptions of methods, their current status and their full history. Each section is written by one to four of the 32 contributors of the text, with each contributor writing on his/her expertise. Contributors include: Fitzmaurice, Verbeke, Molenberghs, Brumback, Carroll, Diggle, Little, Muller, Robins, among others.
If a section is not thorough enough for your liking, the authors refer you to a plethora of relevant papers for just about everything that is stated in the text. This makes it incredibly easy to investigate further into a given topic. Furthermore, the references are fairly current, with some sections including references up to the year 2007.
As I eluded to before, not only does each chapter cover different methodologies for analyzing longitudinal data, but also includes a history of research regarding a method -- what we used before those methods were introduced, why they were lacking, how the current methods address some of those problems, and what directions exist for further research. This gives the reader a picture of how the field has evolved and adapted to problems that longitudinal data analyses presents researchers. Some may find this uninteresting, but to see the historical research process for some of these methods, in my opinion, illustrates how the field has reached the point it is today and more generally, how research progresses in the real world.
The non-parametric/semi-parametric regression methods and missing data sections are especially appealing for I am unaware of any other source which compiles all current research in these areas in one volume.
As I say in the title of my review, this is akin to a 'travel-guide.' For example you won't find a derivation of GEE in this text, but you'll find what they are, why they were introduced, what they replaced, why we bother using them, what properties they have, what extensions researchers have come up with, and what we still don't know...and most importantly, you'll be directed to where you can find the derivations of all of these. Thus it won't be your final stop in researching any methodology for longitudinal data, but it most likely turn into your first.
This text is a great picture of what can be done with this series of texts (Handbooks of Modern Statistical Methods). I look forward to future installments.
Click Here to see more reviews about: Longitudinal Data Analysis (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)
Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data.
After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data. Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters. Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature. They seamlessly blend theory with applications and include examples and case studies from various disciplines.
Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future. Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.
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