Joint models for longitudinal and time-to-event data with applications in R
Preface 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...
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Boca Raton :
CRC Press
2012.
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| Edition: | 1st ed |
| Series: | Chapman & Hall/CRC biostatistics series ;
6. |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628482106719 |
Table of Contents:
- Front Cover; Dedication; Preface; 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; A. A Brief Introduction to R; B. The EM Algorithm for Joint Models; C. Structure of the JM Package; References
