JM {JM}R Documentation

Joint Modelling of Longitudinal and Survival Data in R

Description

This package fits shared parameter models for the joint modelling of normal longitudinal responses and event times under a maximum likelihood approach. Various options for the survival model and optimization/integration algorithms are provided.

Details

Package: JM
Type: Package
Version: 0.2-2
Date: 2008-12-15
License: GPL

The package has a single model-fitting function called jointModel, which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by function coxph() or function survreg() of package survival. In addition, the method argument of jointModel() specifies the type of the survival submodel to be fitted and the type of the numerical integration technique; available options are:

"ph-GH"
the time-dependent version of a proportional hazards model with unspecified baseline hazard function. The Gauss-Hermite integration rule is used to approximate the required integrals. (This option corresponds to the joint model proposed by Wulfsohn and Tsiatis, 1997)
"weibull-GH"
the Weibull model under the accelerated failure time formulation. The Gauss-Hermite integration rule is used to approximate the required integrals.
"ch-GH"
an additive log cumulative hazard model, in which the log cumulative baseline hazard is approximated using B-splines. The Gauss-Hermite integration rule is used to approximate the required integrals.
"ch-Laplace"
an additive log cumulative hazard model, in which the log cumulative baseline hazard is approximated using B-splines. A fully exponential Laplace approximation method is used to approximate the required integrals (Rizopoulos et al., 2008).

Author(s)

Dimitris Rizopoulos

Maintainer: Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl>

References

Henderson, R., Diggle, P. and Dobson, A. (2000) Joint modelling of longitudinal measurements and event time data. Biostatistics 1, 465–480.

Rizopoulos, D., Verbeke, G. and Lesaffre, E. (2009) Fully exponential Laplace approximation for the joint modelling of survival and longitudinal data. Journal of the Royal Statistical Society, Series B, to appear.

Tsiatis, A. and Davidian, M. (2004) Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica 14, 809–834.

Wulfsohn, M. and Tsiatis, A. (1997) A joint model for survival and longitudinal data measured with error. Biometrics 53, 330–339.

See Also

jointModel


[Package JM version 0.2-2 Index]