Analysis of Longitudinal Data

by Peter J. Diggle, Kung-Yee Liang, Scott L. Zeger

This book provides a self-contained account of a wide range of statistical methods for the analysis of longitudinal data. Emphasizing the biomedical and agricultural sciences, the book covers each method's applicability and underlying statistical theory. Major topics design
considerations, exploratory methods of analysis, linear models for continuous data, generalized linear models for discrete data, and models and methods for handling data with missing values. Worked examples are presented throughout and an appendix covers some basic statistical principles. This
cogent and clear text will be welcomed by students across a wide range of the sciences.

Genres: Textbooks

Hardcover

First published January 1, 1994

Reviews

[David · Rating 3 out of 5]

Despite Diggle's distinguished reputation, I have never found this book particularly helpful or illuminating. This may reflect the fact that most of the data I dealt with were not particularly amenable to the GEE approach.

[Anna Hepworth]

I've classified this as abandoned/unfinished as part of my 2018 amnesty on trying to finish everything I've started. It isn't a commentary on this book, but more that I have over 30 books 'currently reading' and I want to get that down to two (one fiction, one non-fiction). I'll be putting this disclaimer on a number of books :(

Technically, this probably isn't 'unfinished' because I've read most/all of the book in the past, before I started recording things here, but the plan had been to read it straight through.