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.

The package has a single model-fitting function named 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. For the longitudinal process only linear mixed effects models are currently available. For the event process, the method argument of jointModel() specifies the type survival model to be fitted and the type of the numerical integration method. Available options are:

• method = ‘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, Biometrics).

• method = ‘piecewise-PH-GH’: a relative risk model with a piecewise-constant relative risk function. The Gauss-Hermite integration rule is used to approximate the required integrals.

• method = ‘weibull-AFT-GH’: the Weibull model under the accelerated failure time formulation. The Gauss-Hermite integration rule is used to approximate the required integrals.

• method = ‘weibull-PH-GH’: the Weibull model under the relative risk formulation. The Gauss-Hermite integration rule is used to approximate the required integrals.

• method = ‘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.

• method = ‘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.

Current Version: 0.4-0

Author: Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

Maintainer: Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl

Depends: R (>= 2.7.0), MASS, nlme, splines, survival

License: GPL (>= 2)

• Methods for Basic Generics: the function jointModel() return objects of class jointModel, for which the following methods are available: print(), coef(), fixef(), ranef(), fitted(), residuals(), summary(), plot(), vcov(), and logLik(). A detailed description of these functions is available at the on-line help files.

• Optimization Algorithms: for all survival models (except for the time-dependent proportional hazards model), the optimization algorithm starts with EM iterations, and if convergence is not achieved, it switches to quasi-Newton iterations until convergence.

• MI Residuals: a new method for calculating residuals and producing diagnostics plots in joint models. This is based on the idea of multiply imputing the missing longitudinal responses under the fitted joint model, creating thus random versions of the complete data set. These complete data sets can then be used to extract conclusions regarding the modelling assumptions, and how these assumptions are affected by the nonrandom dropout.

• Dynamic Prediction: function survfitJM() can be used to provide predictions and dynamic predictions of survival probabilities for new subjects in the study, taking into account their longitudinal history and baseline covariates.

• Download latest version from CRAN.

• The Reference manual can be found here.

• The changes in the current and previous versions of the package can be found here.

• A draft paper presenting the features of the package is available here

• Sample Analysis of the AIDS data set

• Sample Analysis of the PBC data set

• MI Residuals for the AIDS data set

• MI Residuals for the PBC data set

If you have any questions/suggestions please include them here.