1st Edition

Joint Modeling of Longitudinal and Time-to-Event Data

By Robert Elashoff, Gang li, Ning Li Copyright 2017
    261 Pages
    by Chapman & Hall

    262 Pages 50 B/W Illustrations
    by Chapman & Hall

    261 Pages 50 B/W Illustrations
    by Chapman & Hall

    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.

    Introduction and Examples

    Methods for Ignorable Missing Data
    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 Topics
    Joint 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


    A Software to Implement Joint Models




    Robert Elashoff, Gang Li, Ning Li

    "This book is a comprehensive state-of-the-art treatment of joint models for time-to-event and longitudinal data with numerous applications to real-world problems. … [T]his book is a comprehensive review of the existing literature on joint models, including most extensions of these models, fully parametric or not, for multiple events and multiple markers with a special focus on missingness problems and details about various estimation methods. By emphasizing the most advanced methods, this book usefully completes existing monographs on joint models and will be a helpful reference book for researchers in biostatistics and experienced statisticians, while applied statisticians could also be interested thanks to the numerous examples of real data analyses."
    —Helene Jacqmin-Gadda, University of Bordeaux, in Biometrics, March 2018

    "This book provides an extensive survey of research performed on the subject of joint models in longitudinal and time-to-event data. … The authors’ expertise in this area shines through their careful attention to detail in presenting the wide variety of settings in which these models can be applied. Overall, I consider the book to be a valuable and rich resource for introducing and promoting this relatively new area of research. … Where this book primarily succeeds is in the great care taken by the authors in walking through the necessary details of these joint models and the breadth of topics they cover. When topics are left out, the authors refer to a large body of literature to which the interested reader can look to further their understanding. …
    I would recommend it either as a handy reference for researchers or as a graduate level reference text in a specialized course … [I]t is truly rich with useful content that can be extracted and applied with due diligence. …. I certainly consider it a valuable addition to my bookshelf for personal reference and, should the need arise, I would be happy to refer it to