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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