Joint Models for Longitudinal and Time-to-Event Data, with Applications in R (original) (raw)

Abstract

The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in the recent years. After the seminal papers by Faucett and Thomas (1996) and Wulfsohn and Tsiatis (1997) who introduced what it could be nowadays called the standard joint model, there has been an explosion of developments in this field. Numerous papers have appeared proposing several extensions of the standard joint model, including among others, the flexible modeling of longitudinal trajectories, the incorporation of latent classes to account for population heterogeneity, the consideration of multiple longitudinal markers, accommodating multiple failure times, and the calculation of dynamic predictions and accuracy measures The primary goal of this monograph is to introduce this joint modeling framework. In particular, we will focus on the type of research questions joint models attempt to answer and the circumstances under which these models are appropriate to answer those questions, we will explain which are the key assumptions behind them, and how they can be optimally utilized to extract relevant information from the data. To facilitate exposition of all the theoretical material covered in this book, all illustrations put forward are available within the R software environment for statistical computing and graphics, using package JM.

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