Description Usage Arguments Value Author(s) References Examples

View source: R/survCompeting.R

This package describes an algorithm for producing fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible masking of the failure cause in a competing risks environment. It is a generalization of Turnbull's (1976) classic univariate algorithm. The algorithm was published in Adamic et al. (2010) and Adamic & Caron (2014).

1 | ```
survCompeting(data, tau, n, nc, epsilon)
``` |

`data` |
input matrix of probabilities |

`tau` |
the vector of time points corresponding to columns in input matrix |

`n` |
the number of intervals in the dataset corresponding to rows in input matrix |

`nc` |
the number of causes (competing risks) |

`epsilon` |
small predermined value > 0 |

`Yj` |
estimated number at risk at time tau_j |

`djc` |
estimated number of events occuring at time tau_j by cause c |

`pjc` |
estimated probability for risk at time tau_j by cause c |

`djList` |
the list of d_j for every cause c |

`pjList` |
the list of p_j for every cause c |

`pjListold` |
the list of p_j for every cause c in the (iter - 1) iteration |

`iter` |
the number of iterations in the algorithm |

Peter Adamic, Alicja Wolny-Dominiak

1. Adamic, P., Caron, S. (2014),
"SC-CR Algorithms with Informative Masking",
Scandinavian Actuarial Journal, 2014(4), 339-351.

2. Adamic, P., Dixon, S., Gillis, D. (2010),
"Multiple Decrement Modeling in the Presence of Interval
Censoring and Masking", Scandinavian Actuarial Journal, 2010(4), 312-327.

3. Adamic, P., Ouadah, S. (2009),
"A Kernel Method for Modeling Interval Censored Competing
Risks", South African Statistical Journal, 43(1), 1-20.

4. Turnbull, B. (1976). The Empirical Distribution Function with Arbitrarily Grouped, Censored and Truncated Data, Journal of the Royal Statistical Society. Series B (Methodological), 38(3), 290-295.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
data(censoredMatrix)
df <- inputM(censoredMatrix)
res <- survCompeting(df$input, df$tau, 8, 3, 0.01)
res
#summary
round(res$Yj, 2)
round(res$djc, 2)
round(res$pjc, 2)
res$iter
sum(unlist(res$pjList))
sum(unlist(res$pjListold))
``` |

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