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Joint Modeling of ­Longitudinal and ­Time-to-Event Data
Chapman & Hall/CRC Monographs on Statistics and Applied Probability

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Format
Hardback, 262 pages
Other Formats Available

Paperback : £45.35

Published
United States, 29 July 2016


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

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Product Description


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

Show more
Product Details
EAN
9781439807828
ISBN
1439807825
Other Information
37 Tables, black and white; 50 Illustrations, black and white
Dimensions
23.4 x 15.8 x 1.8 centimeters (0.47 kg)

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

About the Author

Robert Elashoff, Gang Li, Ning Li

Reviews

"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

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