joineRML-package: joineRML: Joint Modelling of Multivariate Longitudinal Data...
In joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
joineRML-package R Documentation
joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Description
Fits the joint model proposed by Henderson and colleagues (2000) \Sexpr[results=rd]{tools:::Rd_expr_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).
Author(s)
Maintainer: Graeme L. Hickey graemeleehickey@gmail.com (ORCID)
Authors:
Pete Philipson peter.philipson1@newcastle.ac.uk (ORCID)
Ruwanthi Kolamunnage-Dona kdrr@liverpool.ac.uk (ORCID)
Alessandro Gasparini alessandro.gasparini@ki.se (ORCID)
Other contributors:
Andrea Jorgensen aljorgen@liverpool.ac.uk (ORCID) [contributor]
Paula Williamson p.r.williamson@liverpool.ac.uk (ORCID) [contributor]
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl (data/renal.rda, R/hessian.R, R/vcov.R) [contributor, data contributor]
Medical Research Council (Grant number: MR/M013227/1) [funder]
See Also
Useful links:
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| joineRML-package | R Documentation |
joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes
Description
Fits the joint model proposed by Henderson and colleagues (2000) \Sexpr[results=rd]{tools:::Rd_expr_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).
Author(s)
Maintainer: Graeme L. Hickey graemeleehickey@gmail.com (ORCID)
Authors:
Pete Philipson peter.philipson1@newcastle.ac.uk (ORCID)
Ruwanthi Kolamunnage-Dona kdrr@liverpool.ac.uk (ORCID)
Alessandro Gasparini alessandro.gasparini@ki.se (ORCID)
Other contributors:
Andrea Jorgensen aljorgen@liverpool.ac.uk (ORCID) [contributor]
Paula Williamson p.r.williamson@liverpool.ac.uk (ORCID) [contributor]
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl (data/renal.rda, R/hessian.R, R/vcov.R) [contributor, data contributor]
Medical Research Council (Grant number: MR/M013227/1) [funder]
See Also
Useful links:
R Package Documentation
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