JM {JM}

Joint Modeling of Longitudinal and Time-to-Event Data in R

Description

This package fits shared parameter models for the joint modeling 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:	1.5-2
Date:	2022-08-08
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 either 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:

"Cox-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-PH-GH"

the Weibull model under the proportional hazards formulation. The Gauss-Hermite integration rule is used to approximate the required integrals.

"weibull-AFT-GH"

the Weibull model under the accelerated failure time formulation. The Gauss-Hermite integration rule is used to approximate the required integrals.

"piecewise-PH-GH"

a proportional hazards model with a piecewise constant baseline risk function. The Gauss-Hermite integration rule is used to approximate the required integrals.

"spline-PH-GH"

a proportional hazards model, in which the log 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., 2009).

For all the above mentioned options (except the last one), a pseudo-adaptive Gauss-Hermite integration rule is also available (Rizopoulos, 2012b). This is much faster than the classical Gauss-Hermite rule, and in several simulations it has been shown to perform equally well (though its performance is still under investigation).

The package also offers several utility functions that can extract useful information from fitted joint models. The most important of those are included in the See also section below.

Author(s)

Dimitris Rizopoulos

Maintainer: Dimitris Rizopoulos <[e-mail]>

References

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

Rizopoulos, D. (2012a) Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC.

Rizopoulos, D. (2012b) Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule. Computational Statistics and Data Analysis 56, 491–501.

Rizopoulos, D. (2011) Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics 67, 819–829.

Rizopoulos, D. (2010) JM: An R package for the joint modelling of longitudinal and time-to-event data. Journal of Statistical Software 35 (9), 1–33. doi:10.18637/jss.v035.i09

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 71, 637–654.

Rizopoulos, D., Verbeke, G. and Molenberghs, G. (2010) Multiple-imputation-based residuals and diagnostic plots for joint models of longitudinal and survival outcomes. Biometrics 66, 20–29.

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, survfitJM, rocJM, aucJM, dynCJM, prederrJM, predict