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.
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