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

Graeme  Hickey; Pete Philipson; Andrea Jorgensen; Ruwanthi Kolamunnage-Dona

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

Graeme Hickey, Pete Philipson, Andrea Jorgensen, Ruwanthi Kolamunnage-Dona

Mathematics, Physics and Electrical Engineering

Research output: Other contribution

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Abstract

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 is funded by the Medical Research Council (Grant number MR/M013227/1).

Original language	English
Publisher	CRAN R project
Place of Publication	CRAN repository for R software packages
Publication status	Published - 27 Dec 2016

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Package manual for joineRMLOther version, 253 KBLicence: GNU GPL
Technical details of joineRML softwareOther version, 277 KBLicence: GNU GPL
JoineRML packageOther version, 253 KB

https://cran.r-project.org/web/packages/joineRML/index.html

Cite this

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title = "Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes",

abstract = "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 is funded by the Medical Research Council (Grant number MR/M013227/1).",

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AU - Hickey, Graeme

AU - Philipson, Pete

AU - Jorgensen, Andrea

AU - Kolamunnage-Dona, Ruwanthi

PY - 2016/12/27

Y1 - 2016/12/27

N2 - 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 is funded by the Medical Research Council (Grant number MR/M013227/1).

AB - 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 is funded by the Medical Research Council (Grant number MR/M013227/1).

UR - https://cran.r-project.org/web/packages/joineRML/index.html

M3 - Other contribution

PB - CRAN R project

CY - CRAN repository for R software packages

ER -

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

Abstract

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