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.

Table of Contents:
Introduction and ExamplesIntroduction Methods for Ignorable Missing Data Introduction Missing Data Mechanisms Linear and Generalized Linear Mixed Models Generalized Estimating Equations Fruther topics Time-to-event data analysis Right censoring Survival function and hazard function Estimation of a survival function Cox's semiparametric multiplicative hazards models Accelerated failure time models with time-independent covariates Accelerated failure time model with time-dependent covariates Methods for competing risks data Further topics Overview of Joint Models for Longitudinal and Time-to-Event Data Joint Models of Longitudinal Data and an Event time Joint Models with Discrete Event Times and Monotone Missingness Longitudinal Data with Both Monotone and Intermittent Missing Values Event Time Models with Intermittently Measured Time Dependent Covariates Longitudinal Data with Informative Observation Times Dynamic Prediction in Joint Models Joint Models for Longitudinal Data and Continuous Event Times from Competing Risks Joint Alaysis of Longitudinal Data and Competing Risks A Robust Model with t-Distributed Random Errors Ordinal Longitudinal Outcomes with Missing Data Due to Multiple Failure Types Bayesian Joint Models with Heterogeneous Random Effects Accelerated Failure Time Models for Competing Risks Joint Models for Multivariate Longitudinal and Survival Data Joint Models for Multivariate Longitudinal Outcomes and an Event Time Joint Models for Recurrent Events and Longitudinal Data Joint Models for Multivariate Survival and Longitudinal Data Further TopicsJoint Models and Missing Data: Assumptions, Sensitivity Analysis, and Diagnostics Variable Selection in Joint Models Joint Multistate Models Joint Models for Cure Rate Survival Data Sample Size and Power Estimation for Joint Models Appendices A Software to Implement Joint Models Bibliography Index