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Joint Models for Longitudinal and Time-to-Event Data on Hardback by Dimitris Rizopoulos
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Joint Models for Longitudinal and Time-to-Event Data

With Applications in R
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Description

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/

Author Biography:

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.
Release date Australia
June 22nd, 2012
Audience
  • Professional & Vocational
Illustrations
6 Tables, black and white; 36 Illustrations, black and white
Pages
278
Dimensions
156x235x20
ISBN-13
9781439872864
Product ID
19865770

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Joint Models for Longitudinal and Time-to-Event Data by Dimitris Rizopoulos
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