joineRML (0.4.1)

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Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes.

Fits the joint model proposed by Henderson and colleagues (2000) , 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).

Maintainer: Graeme L. Hickey
Author(s): Graeme L. Hickey [cre, aut], Pete Philipson [aut], Andrea Jorgensen [aut], Ruwanthi Kolamunnage-Dona [aut], Paula Williamson [ctb], Dimitris Rizopoulos [ctb, dtc] (data/renal.rda, R/hessian.R, R/vcov.R), Alessandro Gasparini [ctb], Medical Research Council [fnd] (Grant number: MR/M013227/1)

License: GPL-3 | file LICENSE

Uses: cobs, doParallel, foreach, ggplot2, lme4, MASS, Matrix, mvtnorm, nlme, randtoolbox, Rcpp, survival, JM, testthat, knitr, joineR, rmarkdown

Released 4 months ago.

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