Joint Modeling of Longitudinal and Time-to-Event Data

Robert Elashoff, Gang li, Ning Li



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.

Features

• Longitudinal data analysis with non-ignorable monotone and intermittent missing data.
• Event time models with intermittently measured time-dependent covariates.
• Longitudinal studies with informative observation times.
• Joint models for competing risks, multivariate longitudinal, and multivariate survival outcomes
• Dynamic prediction
• Modeling longitudinal data shortly before death