This repository contains the source files for the R package
**JMbayes**. This package fits joint models for
longitudinal and time-to-event data under a Bayesian approach using
MCMC. 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 package contains two main joint-model-fitting functions,
`jointModelBayes()`

and `mvJointModelBayes()`

with
similar syntax but different capabilities.

`jointModelBayes()`

It can fit joint models for a single longitudinal outcome and a time-to-event outcome.

The user can specify her own density function for the longitudinal responses using argument

`densLong`

(default is the normal pdf). Among others, this allows to fit joint models with categorical and left-censored longitudinal responses and robust joint models with Studentâ€™s-t error terms. In addition, using the`df.RE`

argument, the user can also change the distribution of the random effects from multivariate normal to a multivariate Studentâ€™s-t with prespecified degrees of freedom.For the survival outcome a relative risk models is assumed with a B-spline approximation for the baseline hazard (penalized (default) or regression splines can be used). Left-truncation and exogenous time-varying covariates can also be accommodated.

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

`extraForm`

,`param`

). 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`transFun`

interactions terms, nonlinear terms (polynomials, splines) can be considered.

`mvJointModelBayes()`

It can fit joint models for multiple longitudinal outcomes and a time-to-event outcome.

The longitudinal part of the joint model is a multivariate generalized linear mixed effects models, currently allowing for normal, binary and Poisson outcomes. This model is first fitted using function

`mvglmer()`

.For the survival outcome a relative risk models is assumed with a B-spline approximation for the baseline hazard (penalized (default) or regression splines can be used). Left-truncation, interval censored data and exogenous time-varying covariates can also be accommodated.

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 (argument

`Formulas`

). 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 limitless terms can be included. In addition, using argument`Interactions`

allows to include interactions terms of the longitudinal components with other observed factors. A special case for this argument is to use function`tve()`

that allows for time-varying regression coefficients in the relative risk model. Furthermore, argument`transFuns`

allows to transform the longitudinal components using some pre-defined transformation function (i.e.,`exp()`

,`expit()`

,`log`

,`sqrt()`

).The aforementioned features are illustrated in the Multivariate Joint Models vignette.

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.Function

`runDynPred()`

invokes a shiny application that can be used to streamline the calculation of dynamic predictions for models fitted by**JMbayes**.

Vignettes are available in the `doc`

directory:

Multivariate_Joint_Models.html illustrates the basic capabilities of

`mvJointModelBayes()`

.Dynamic_Predictions.html illustrates how dynamic predictions from multivariate joint models can be computed and evaluated.