Joint Modeling of Longitudinal and Time-to-Event Data

Robert Elashoff, Gang Li, Ning Li

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