Overview

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.

Full Product Details

9781439807828

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Reviews

This book is a comprehensive state-of-the-art treatment of joint models for time-to-event and longitudinal data with numerous applications to real-world problems. ... [T]his book is a comprehensive review of the existing literature on joint models, including most extensions of these models, fully parametric or not, for multiple events and multiple markers with a special focus on missingness problems and details about various estimation methods. By emphasizing the most advanced methods, this book usefully completes existing monographs on joint models and will be a helpful reference book for researchers in biostatistics and experienced statisticians, while applied statisticians could also be interested thanks to the numerous examples of real data analyses. -Helene Jacqmin-Gadda, University of Bordeaux, in Biometrics, March 2018 This book provides an extensive survey of research performed on the subject of joint models in longitudinal and time-to-event data. ... The authors' expertise in this area shines through their careful attention to detail in presenting the wide variety of settings in which these models can be applied. Overall, I consider the book to be a valuable and rich resource for introducing and promoting this relatively new area of research. ... Where this book primarily succeeds is in the great care taken by the authors in walking through the necessary details of these joint models and the breadth of topics they cover. When topics are left out, the authors refer to a large body of literature to which the interested reader can look to further their understanding. ... I would recommend it either as a handy reference for researchers or as a graduate level reference text in a specialized course ... [I]t is truly rich with useful content that can be extracted and applied with due diligence. .... I certainly consider it a valuable addition to my bookshelf for personal reference and, should the need arise, I would be happy to refer it to

"""This book is a comprehensive state-of-the-art treatment of joint models for time-to-event and longitudinal data with numerous applications to real-world problems. … [T]his book is a comprehensive review of the existing literature on joint models, including most extensions of these models, fully parametric or not, for multiple events and multiple markers with a special focus on missingness problems and details about various estimation methods. By emphasizing the most advanced methods, this book usefully completes existing monographs on joint models and will be a helpful reference book for researchers in biostatistics and experienced statisticians, while applied statisticians could also be interested thanks to the numerous examples of real data analyses."" —Helene Jacqmin-Gadda, University of Bordeaux, in Biometrics, March 2018 ""This book provides an extensive survey of research performed on the subject of joint models in longitudinal and time-to-event data. … The authors’ expertise in this area shines through their careful attention to detail in presenting the wide variety of settings in which these models can be applied. Overall, I consider the book to be a valuable and rich resource for introducing and promoting this relatively new area of research. … Where this book primarily succeeds is in the great care taken by the authors in walking through the necessary details of these joint models and the breadth of topics they cover. When topics are left out, the authors refer to a large body of literature to which the interested reader can look to further their understanding. … I would recommend it either as a handy reference for researchers or as a graduate level reference text in a specialized course … [I]t is truly rich with useful content that can be extracted and applied with due diligence. …. I certainly consider it a valuable addition to my bookshelf for personal reference and, should the need arise, I would be happy to refer it to"

...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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Robert Elashoff, Gang Li, Ning Li

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