Description
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
