swMATH ID: 19419
Software Authors: Graeme L. Hickey, Pete Philipson, Andrea Jorgensen, Ruwanthi Kolamunnage-Dona, Paula Williamson, Dimitris Rizopoulos
Description: R package joineRML. Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes. Fits the joint model proposed by Henderson and colleagues (2000) <<a href=”http://dx.doi.org/10.1093/biostatistics/1.4.465”>doi:10.1093/biostatistics/1.4.465</a>>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project is funded by the Medical Research Council (Grant number MR/M013227/1).
Homepage: https://cran.r-project.org/web/packages/joineRML/index.html
Source Code:  https://github.com/cran/joineRML
Dependencies: R
Keywords: CRAN; R package; Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Related Software: JM; nlme; joineR; JMbayes; R; frailtypack; merlin; JMfit; JSM; bamlss; coxme; Stata; SAS; lcmm; WinBUGS; Statmod; Rcpp; RcppEigen; PyTorch; NumPy
Cited in: 5 Publications

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