# Predicted cumulative incidence of event according to a profile of covariates

### Description

This function computes the predicted cumulative incidence of each cause of event according to a profile of covariates from a joint latent class model. Confidence bands can be computed by a Monte-Carlo method.

### Usage

1 |

### Arguments

`x` |
an object inheriting from class |

`time` |
a vector of times at which the cumulative incidence is calculated |

`draws` |
optional boolean specifying whether a Monte Carlo approximation of the posterior distribution of the cumulative incidence is computed and the median, 2.5% and 97.5% percentiles are given. Otherwise, the predicted cumulative incidence is computed at the point estimate. By default, draws=FALSE. |

`ndraws` |
if draws=TRUE, ndraws specifies the number of draws that should be generated to approximate the posterior distribution of the predicted cumulative incidence. By default, ndraws=2000. |

`...` |
further arguments, in particular values of the covariates specified in the survival part of the joint model. |

### Value

An object of class `cuminc`

containing as many matrices as profiles defined by the covariates
values. Each of these matrices contains the event-specific cumulative incidences in
each latent class at the different times specified.

### Author(s)

Viviane Philipps and Cecile Proust-Lima

### See Also

`Jointlcmm`

,`plot.Jointlcmm`

,`plot.cuminc`

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.