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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 cov...
| Autor principal: | |
|---|---|
| Formato: | Libro |
| Lenguaje: | English |
| Publicado: |
Boca Raton :
CRC Press,
©2012.
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| Colección: | Chapman & Hall/CRC biostatistics series ;
6 |
| Materias: | |
| Acceso en línea: | http://www.crcnetbase.com/isbn/9781439872864 http://www.crcnetbase.com/isbn/9781439872871 http://site.ebrary.com/id/10574354 http://www.myilibrary.com?id=530520 http://lib.myilibrary.com/detail.asp?id=530520 http://alltitles.ebrary.com/Doc?id=10574354 http://marc.crcnetbase.com/isbn/9781439872871 |
MARC
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| 100 | 1 | |a Rizopoulos, Dimitris |9 416093 | |
| 245 | 1 | 0 | |a Joint models for longitudinal and time-to-event data |b : with applications in R |c / Dimitris Rizopoulos. |
| 260 | |a Boca Raton : |b CRC Press, |c ©2012. | ||
| 300 | |a xiv, 261 páginas : |b gráficas ; |c 24 cm. | ||
| 490 | 0 | |a Chapman & Hall/CRC biostatistics series ; |v 6 | |
| 504 | |a Incluye bibliografía (páginas 239-255) e índice. | ||
| 505 | 0 | |a 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. | |
| 520 | |a "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 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"-- | ||
| 650 | 4 | |a Análisis numérico |x Procesamiento de datos. | |
| 650 | 4 | |a R (Lenguaje de programación para computadoras) | |
| 856 | 4 | 0 | |u http://www.crcnetbase.com/isbn/9781439872864 |
| 856 | 4 | 0 | |u http://www.crcnetbase.com/isbn/9781439872871 |
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| 856 | 4 | 0 | |u http://alltitles.ebrary.com/Doc?id=10574354 |
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