Joint Models for Longitudinal and Time-To-Event Data _ with Applications in R (2012) Pdf Free Download

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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 the focus is on the survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when the focus is on the longitudinal outcome and we wish to correct for nonrandom dropout. Due to the capability of joint models 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 on 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. Motivated by the background and collaborations of the author, these examples come from the medical field and in particular from biomarker research. Nevertheless, joint models of this type can be employed whenever there is interest in the association between a longitudinal and an event time outcome, and therefore have numerous applications in other fields as well. In addition, several extensions are presented, including among others, different types of association structure between the longitudinal and survival outcomes, inclusion of stratification factors, incorporation of lagged effects, the handling of exogenous time-dependent covariates, and competing risks settings. For the longitudinal part, primary focus is given on continuous longitudinal outcomes and the use of linear mixed-effects models.

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