Joint models for longitudinal and time-to-event data : with applications in R
- Dimitris Rizopoulos.
- Boca Raton : CRC Press, 2012.
- Physical description
- xiv, 261 p. : ill. ; 24 cm.
- Chapman & Hall/CRC biostatistics series (Unnumbered)
Math & Statistics Library
QA279 .R59 2012
- Unknown QA279 .R59 2012
- Rizopoulos, Dimitris.
- Includes bibliographical references (p. 239-255) and index.
- 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.
- (source: Nielsen Book Data)
- Publisher's 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 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/.
(source: Nielsen Book Data)
- Publication date
- Chapman & Hall/CRC biostatistics series
- 9781439872864 (hardback)
- 1439872864 (hardback)