02763cam a2200325 a 4500 17312879 BD-DhUL 20150809125700.0 120522s2012 flua b 001 0 eng 2012014570 9781439872864 (hardback) DLC DLC BD-DhUL pcc QA279 .R59 2012 518 23 RIJ MAT029000 MED028000 bisacsh Rizopoulos, Dimitris. Joint models for longitudinal and time-to-event data : with applications in R / Dimitris Rizopoulos. Boca Raton : CRC Press, 2012. xiv, 261 p. : ill. ; 24 cm. GBP 55.99 Chapman & Hall/CRC biostatistics series ; 6 Includes bibliographical references and index. "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"-- Provided by publisher. Numerical analysis Data processing. R (Computer program language) MATHEMATICS / Probability & Statistics / General. bisacsh MEDICAL / Epidemiology. bisacsh ddc BK 41451 41451 0 0 ddc 0 518_000000000000000_RIJ 0 NFIC 67786 DUSL DUSL GEN 2015-05-14 Purchased 518 RIJ 491004 2015-08-09 2 2015-08-09 BK 0 0 ddc 0 518_000000000000000_RIJ 0 NFIC 67787 DUSL DUSL GEN 2015-05-14 Purchased 518 RIJ 491003 2015-08-09 1 2015-08-09 BK