joineR: Joint modelling of repeated measurements and time-to-event data

Pete Philipson; Peter Diggle; Ines Sousa; Ruwanthi Kolamunnage-Dona; Paula Williamson; Robin Henderson

joineR: Joint modelling of repeated measurements and time-to-event data

Pete Philipson, Peter Diggle, Ines Sousa, Ruwanthi Kolamunnage-Dona, Paula Williamson, Robin Henderson

Research output: Other contribution

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Abstract

The joineR package implements methods for analysing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-toevent outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure. The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty. Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model.

Original language	English
Publisher	Comprehensive R Archive Network
Place of Publication	United Kingdom
Publication status	Published - 30 Mar 2012

Keywords

R software
longitudinal and survival data
repeated measures
time-to-event data
joint modelling

Access to Document

JoineR vignetteFinal published version, 273 KB
JoineR manualFinal published version, 164 KB

http://cran.r-project.org/web/packages/joineR/index.html

Cite this

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title = "joineR: Joint modelling of repeated measurements and time-to-event data",

abstract = "The joineR package implements methods for analysing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-toevent outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure. The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty. Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model.",

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AU - Sousa, Ines

AU - Kolamunnage-Dona, Ruwanthi

AU - Williamson, Paula

AU - Henderson, Robin

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N2 - The joineR package implements methods for analysing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-toevent outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure. The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty. Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model.

AB - The joineR package implements methods for analysing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-toevent outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure. The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty. Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model.

KW - R software

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M3 - Other contribution

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ER -

joineR: Joint modelling of repeated measurements and time-to-event data

Abstract

Keywords

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