Description Usage Arguments Details References Examples

Provides an implementation to boost joint models for longitudinal and time-to-event outcomes.

1 2 3 4 |

`y` |
Longitudinal outcome including observation at or before event (last). |

`Xl` |
covariate matrix for longitudinal predictor |

`Xls` |
covariate matrix for shared predictor |

`last` |
vector indicating if observation refers to longitduinal (FALSE) or shared predictor (TRUE); i.e. if it is the patient's last observation before event. |

`delta` |
censoring indicator (person had event or not) |

`id` |
1xN vector of subjects |

`time` |
all time points (included in Xls if betatimeind != 0) |

`lambda` |
starting value baseline hazard |

`alpha` |
starting value association parameter |

`mstop_l` |
Stopping iteration longitudinal predictor. |

`mstop_ls` |
Stopping iteration shared predictor. |

`step.length` |
Step length for boosting updates, typical value is 0.1 (default). |

`betatimeind` |
indicating which coefficient in shared predictor is time variable (number), |

Implements a gradient boosting algorithn to fit joint models considering a longitudinal predictor (w.r.t outcome y) and a shared predictor (affecting the longitudinal outcome and the time-to-event outcome). The relation between the two outcomes is described via the association parameter alpha. For more details, see Waldmann et al. (2016).

Waldmann, E., Taylor-Robinson, D., Klein, N., Kneib, T., Pressler, T., Schmid, M., & Mayr, A. (2016). Boosting Joint Models for Longitudinal and Time-to-Event Data. arXiv preprint arXiv:1609.02686.

1 2 3 4 5 6 7 8 9 10 | ```
set.seed(123)
dat <- simJM(n = 400, n_i = 3, alpha = .5,
beta = c(1,2,3), betals = c(2,3,1),
betatimeind = 3, lambda = 0.6)
j1 <- JMboost(y = dat$y, Xl = dat$X, Xls = dat$Xls,
last = dat$last, delta = dat$delta,
id = dat$id, time = dat$time, lambda = 1, alpha = 0.1,
mstop_l = 100, mstop_ls = 100, step.length = 0.1,
betatimeind = 3)
``` |

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