Joint Modeling of Longitudinal and Time-to-Event Data

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.

Book Details

• Authors: Robert Elashoff, Gang li, Ning Li
• Publisher: Chapman & Hall
• eBook ISBN: 9781315357188, 1315357186
• Print ISBN: 9781439807828, 1439807825
• Other ISBNs: 9781315374871, 9780367570576, 9781439807835, 9781315338125