Joint Modeling of Longitudinal and Time-to-Event Data

by Robert Elashoff (Author), Gang li (Author), Ning Li (Author)

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.

Review
"This book provides the most comprehensive and in-depth coverage of the topic of joint modeling of longitudinal and survival data….A unique feature of this book in comparison to other related books or review papers is its broad yet in-depth coverage of the topics in joint modeling, which include: (i) monotone or intermittent non-ignorable missing data triggered by a single event, such as death, or multiple types of events, (ii) intermittently measured time-dependent covariates, which may be further subject to measurement errors, and (iii) longitudinal data with informative observational times. Extension to multivariate longitudinal and/or survival data as well as event-times subject to competing risks are also covered. In addition to theory and methodology, applications are also emphasized in this book, with a collection of available software listed in the Appendix. This is a useful book for anyone wishing to dive into the joint modeling paradigm as well as a good resource for seasoned researchers. It is well suited for a graduate course."
―Jane-Ling Wang, Distinguished Professor of Statistics, University of California, Davis, June 2016

"Many good books are now available that treat the analysis of repeated measurement and time-to-event data as separate topics. However, in many longitudinal studies both types of data are collected and a joint approach is required in order to exploit fully the information in the data. This book is a very welcome addition to the literature on joint modelling. It contains a nice blend of theory and applications, and would be an excellent text for a graduate course in biostatistics or as a manual for practising biostatisticians."
―Peter J Diggle, CHICAS, Lancaster University Medical School, May 2016

"…A clearly well-written book covering a broad range of topics on joint modelling of longitudinal data and time-to-event data that will, without doubt, serve as a valuable reference for researchers interested in this field. At the same time, this timely and comprehensive overview is accessible to those with almost no background in this area and practitioners with a large collection of applications, real data and online data sources. This book could also serve as a valuable textbook for graduate students in statistics or biostatistics due to its balance of methodology and practical examples."
―Jianguo Sun, University of Missouri, May 2016



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