Joint Models for Longitudinal and Time-to-Event Data

With Applications in R
Erscheinungsjahr 2012
ISBN: 978-1-4398-7286-4
Verlag: Taylor & Francis Inc

Buch, Englisch, 275 Seiten, Format (B × H): 164 mm x 239 mm, Gewicht: 539 g

Reihe: Chapman & Hall/CRC Biostatistics Series

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models.

All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.

All the R code used in the book is available at:

http://jmr.r-forge.r-project.org/

Autoren/Hrsg.

Rizopoulos, Dimitris

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik

Inhaltsverzeichnis

Introduction
Inferential Objectives in Longitudinal Studies
Case Studies
Organization of the Book

Analysis of Longitudinal Data
Features of Repeated Measures Data
Linear Mixed Effects Models
Dropout in Longitudinal Studies

Analysis of Time-to-Event Data
Features of Event Time Data
Relative Risk Models
Time-Dependent Covariates

Joint Models for Longitudinal and Time-to-Event Data
The Standard Joint Model
Connection with the Dropout Framework

Extensions of the Standard Joint Model
Parameterizations
Multiple Failure Times
Latent Class Joint Models

Diagnostics
Residuals for the Longitudinal Submodel
Residuals for the Survival Submodel
Random Effects Distribution

Prediction and Accuracy in Joint Models
Dynamic Predictions for the Survival and Longitudinal Outcomes
Effect of the Parameterization on Predictions
Prospective Accuracy Measures for Longitudinal Markers

Über Autor(innen)

Dimitris Rizopoulos is an Assistant Professor at the Department of Biostatistics of the Erasmus University Medical Center in the Netherlands. Dr. Rizopoulos received his M.Sc. in Statistics in 2003 from the Athens University of Economics and Business, and a Ph.D. in Biostatistics in 2008 from the Katholieke Universiteit Leuven.
Dr. Rizopoulos wrote his dissertation, as well as a number of methodological articles on various aspects of joint models for longitudinal and time-to-event data. He currently serves as an Associate Editor for Biometrics and Biostatistics, and has been a guest editor for a special issue in joint modeling techniques in Statistical Methods in Medical Research.