joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Version 0.4.1

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).

Package details

AuthorGraeme 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)
Date of publication2018-01-21 20:11:03 UTC
MaintainerGraeme L. Hickey <[email protected]>
LicenseGPL-3 | file LICENSE
Package repositoryView on CRAN
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joineRML documentation built on Jan. 22, 2018, 1:03 a.m.