joineRML
Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Fits the joint model proposed by Henderson and colleagues (2000) doi:10.1093/biostatistics/1.4.465, 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 was funded by the Medical Research Council (Grant number MR/M013227/1).
- Version0.4.7
- R versionR (≥ 3.6.0)
- LicenseGPL-3
- Needs compilation?Yes
- joineRML citation info
- Last release02/04/2025
Documentation
Team
Graeme L. Hickey
MaintainerShow author detailsDimitris Rizopoulos
Show author detailsRolesContributor, dtcMedical Research Council
Show author detailsRolesfndPaula Williamson
Pete Philipson
Alessandro Gasparini
Show author detailsRolesAuthorRuwanthi Kolamunnage-Dona
Andrea Jorgensen
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- Depends2 packages
- Imports13 packages
- Suggests7 packages
- Linking To2 packages
- Reverse Imports1 package
- Reverse Suggests2 packages