This repository contains the source files for the R package JM. This package fits joint models for longitudinal and time-to-event data using maximum likelihood. These models are applicable in mainly two settings. First, when focus is on the survival outcome and we wish to account for the effect of an endogenous (aka internal) time-dependent covariates measured with error. Second, when focus is on the longitudinal outcome and we wish to correct for nonrandom dropout.
The basic joint-model-fitting function of the package is
jointModel(). This accepts as main arguments a linear mixed
model fitted by function lme() from the nlme
package and a Cox model fitted using function coxph() from
the survival
package.
It can fit joint models for a single continuous longitudinal outcome and a time-to-event outcome.
For the survival outcome a relative risk models is assumed. The
method argument of jointModel() can be used to
define the type of baseline hazard function. Options are a B-spline
approximation, a piecewise-constant function, the Weibull hazard and a
completely unspecified function (i.e., a discrete function with point
masses at the unique event times).
The user has now the option to define custom transformation
functions for the terms of the longitudinal submodel that enter into the
linear predictor of the survival submodel (arguments
derivForm, parameterization). For example, the
current value of the longitudinal outcomes, the velocity of the
longitudinal outcome (slope), the area under the longitudinal profile.
From the aforementioned options, in each model up to two terms can be
included. In addition, using argument InterFact
interactions terms can be considered.
Function survfitJM() computes dynamic survival
probabilities.
Function predict() computes dynamic predictions for
the longitudinal outcome.
Function aucJM() calculates time-dependent AUCs for
joint models, and function rocJM() calculates the
corresponding time-dependent sensitivities and specifies.
Function prederrJM() calculates prediction errors
for joint models.