This book, like many other books, was delivered under tremendous inspiration
and encouragement from my teachers, research collaborators, and students.
My interest in longitudinal data analysis began with a short course taught
jointly by K.Y. Liang and S.L. Zeger at the Statistical Society of Canada
Conference in Acadia University, Nova Scotia, in the spring of 1993. At that
time, I was a first-year PhD student in the Department of Statistics at the
University of British Columbia, and was eagerly seeking potential topics for
my PhD dissertation. It was my curiosity (driven largely by my terrible confusion)
with the generalized estimating equations (GEEs) introduced in the
short course that attracted me to the field of correlated data analysis. I hope
that my experience in learning about it has enabled me to make this book
an enjoyable intellectual journey for new researchers entering the field. Thus,
the book aims at graduate students and methodology researchers in statistics
or biostatistics who are interested in learning the theory and methods of
correlated data analysis.
I have attempted to give a systematic account of regression models and
their applications to the modeling and analysis of correlated data. Longitudinal
data, as an important type of correlated data, has been used as a main
venue for motivation, methodological development, and illustration throughout
the book. Given the many applied books on longitudinal data analysis already
available, this book is inclined more towards technical details regarding
the underlying theory and methodology used in software-based applications.
I hope the book will serve as a useful reference for those who want theoretical
explanations to puzzles arising from data analyses or deeper understanding
of underlying theory related to analyses. This book has evolved from lecture
notes on longitudinal data analysis, and may be considered suitable as a textbook
for a graduate course on correlated data analysis.