Longitudinal studies often incur several problems that challenge
standard statistical methods for data analysis. These problems
include non-ignorable missing data in longitudinal measurements of
one or more response variables, informative observation times of
longitudinal data, and survival analysis with intermittently
measured time-dependent covariates that are subject to measurement
error and/or substantial biological variation. Joint modeling of
longitudinal and time-to-event data has emerged as a novel approach
to handle these issues. Joint Modeling of Longitudinal and
Time-to-Event Data provides a systematic introduction and review of
state-of-the-art statistical methodology in this active research
field. The methods are illustrated by real data examples from a
wide range of clinical research topics. A collection of data sets
and software for practical implementation of the joint modeling
methodologies are available through the book website. This book
serves as a reference book for scientific investigators who need to
analyze longitudinal and/or survival data, as well as researchers
developing methodology in this field. It may also be used as a
textbook for a graduate level course in biostatistics or
statistics.
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