Joint modelling of longitudinal measurements and event time data.

By Robin Henderson, Peter J. Diggle, Angela Dobson. Biostatistics, 2000

993 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8e6f75368f866264ccb2d835820998f6b3ea86cd

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Citations in the corpus

Random Effects Models for Longitudinal Data (2010, methodology): “Joint models with such error structures have been proposed by Wang and Taylor (2001), who postulated an integrated Ornstein-Uhlenbeck process, and by Henderson, Diggle, and Dobson (2000), who considered a latent Gaussian stochastic process shared by both the longitudinal and event processes.”
Handling drop‐out in longitudinal studies (2004, methodology): “These models and methods can be viewed in the broader context of jointly modelling repeated measures and an event time, and in fact some of the models for dealing with drop-out have been used to model jointly evolving processes such as CD4 trajectory and HIV-related death [51, 53, 63–65] and multiva”
Missing data methods in longitudinal studies: a review (2009, methodology): “Another aspect of the problem, discussed by Henderson et al. (2000) , Brown and Ibrahim (2003a, 2003b), Ibrahim et al. (2004), Chen et al. (2004b), Brown et al. (2005), and Chi and Ibrahim (2006, 2007), is the identification of longitudinal markers for survival.”
Maximum likelihood estimation in semiparametric regression models with censored data (2007, methodology): “One may incorporate a within-subject autocorrelation structure in the model, as suggested by Henderson et al. (2000) and Xu and Zeger (2001).”
Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages (2004, methodology): “This article investigates the approach of Henderson, Diggle, and Dobson (2000), who proposed a very ﬂexible joint model that avoids specifying the class variable, yet allows a very broad range of dependencies between the longitudinal responses and the survival endpoints.”
JM: An R package for the joint modelling of longitudinal and time-to-event data (2010, methodology): “The main estimation methods that have been proposed for joint models are (semiparametric) maximum likelihood (Hsieh et al. 2006; Henderson, Diggle, and Dobson 2000; Wulfsohn and Tsiatis 1997) and Bayes using MCMC techniques (Chi and Ibrahim 2006; Brown and Ibrahim 2003; Wang and Taylor 2001; Xu and…”
Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data (2009, methodology): “Common numerical integration techniques such as Gaussian quadrature and Monte Carlo sampling have been successfully applied in the joint modelling framework (Song et al., 2002; Henderson et al., 2000).”
Longitudinal Cognition-Survival Relations in Old and Very Old Age 13-Year Data from the Berlin Aging Study (2006, positive): “Henderson, Diggle, and Dobson (2000) proposed an analysis merging a longitudinal and a survival process via a random Gaussian process (more details follow in the Method section).”
Geostatistical inference under preferential sampling (2010, background): “See, for example, Wulfsohn and Tsiatis (1997) or Henderson et al. (2000).”
Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data (2011, methodology): “Therefore, we follow here the more traditional formulation that assumes continuous random effects to underlie both processes (Wulfsohn and Tsiatis, 1997; Henderson, Diggle, and Dobson, 2000).”
Asymptotic results for maximum likelihood estimators in joint analysis of repeated measurements and survival time (2005, methodology): “Our proofs can be generalized to obtaining the asymptotic properties of the maximum likelihood estimators in other joint models, for example, the joint models of repeated measurements and multivariate survival times which were discussed by Li and Lin [17] and Huang, Zeger, Anthony and Garrett [14], ”
Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review (2017, methodology): “While the integration of such repeated observations is challenging from a statistical perspective, methods do exist.(129) Two of the largest challenges in EHR-based studies are the presence of missing data and informative presence.”
Shared parameter models under random effects misspecification (2008, background): “The consideration of two separate random-effects is in the spirit of the approach proposed by Henderson et al. (2000) who postulate a bivariate Gaussian latent process shared by the two processes.”