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Joint models for longitudinal and time-to-event data : with applications in R

Author: Dimitris Rizopoulos
Publisher: Boca Raton : CRC Press, [2012] ©2012
Series: Chapman & Hall/CRC biostatistics series.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"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
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Genre/Form: Electronic books
Additional Physical Format: Rizopoulos, Dimitris.
Joint models for longitudinal and time-to-event data.
Boca Raton : CRC Press, 2012
(DLC) 2012014570
(OCoLC)698322708
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Dimitris Rizopoulos
ISBN: 9781439872864 1439872864 9781439872871 1439872872 1299992692 9781299992696
OCLC Number: 796662478
Language Note: Text in English.
Description: 1 online resource (255 pages)
Contents: 1. Introduction --
2. Longitudinal data analysis --
3. Analysis of event time data --
4. Joint models for longitudinal and time-to-event data --
5. Extensions of the standard joint model --
6. Joint model diagnostics --
7. Prediction and accuracy in joint models.
Series Title: Chapman & Hall/CRC biostatistics series.
Responsibility: Dimitris Rizopoulos.

Abstract:

"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/"

"Joint models for longitudinal and time-to-event data have become a valuable tool in the analysis of follow-up data. These models are applicable mainly in two settings: First, when focus is in the survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. Due to their capability to provide valid inferences in settings where simpler statistical tools fail to do so, and their wide range of applications, the last 25 years have seen many advances in the joint modeling field. Even though interest and developments in joint models have been widespread, information about them has been equally scattered in articles, presenting recent advances in the field, and in book chapters in a few texts dedicated either to longitudinal or survival data analysis. However, no single monograph or text dedicated to this type of models seems to be available. The purpose in writing this book, therefore, is to provide an overview of the theory and application of joint models for longitudinal and survival data. In the literature two main frameworks have been proposed, namely the random effects joint model that uses latent variables to capture the associations between the two outcomes (Tsiatis and Davidian, 2004), and the marginal structural joint models based on G estimators (Robins et al., 1999, 2000). In this book we focus in the former. Both subfields of joint modeling, i.e., handling of endogenous time-varying covariates and nonrandom dropout, are equally covered and presented in real datasets"--

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"Overall, the book provides a nice introduction to joint models and the R package "JM". It is well written, readable, and comprehensive. With the availability of the R package for joint models, it is Read more...

 
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