cfc.survreg {CFC} | R Documentation |

## Cause-specific competing-risk survival analysis, using parametric survival regression models

### Description

Convenient function to build cause-specific, parametric survival models using the survival package. This is followed by application of `cfc`

function to produce cumulative incidence functions.

### Usage

```
cfc.survreg(formula, data, newdata = NULL, dist = "weibull"
, control = survreg.control(), tout, Nmax = 100L
, rel.tol = 1e-05)
```

### Arguments

`formula` |
Survival formula with a multi-state status variable. See |

`data` |
Data frame containing variables listed in |

`newdata` |
Data frame of structure similar to |

`dist` |
One of |

`control` |
List of |

`tout` |
Time points, along which to produce the cumulative incidence curves. |

`Nmax` |
Maximum number of subdivisions to be used in the |

`rel.tol` |
Threshold for relative error in |

### Value

A list with the following elements:

`K` |
Number of causes. |

`formulas` |
List of formulas used in each of the |

`regs` |
List of all cause-specific regression objects returned by |

`tout` |
Same as input. |

`cfc` |
An object of class |

### Author(s)

Mansour T.A. Sharabiani, Alireza S. Mahani

### References

Mahani A.S. and Sharabiani M.T.A. (2019). Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC. Journal of Statistical Software, 89(9), 1-29. doi:10.18637/jss.v089.i09

### See Also

### Examples

```
data(bmt)
formul <- Surv(time, cause) ~ platelet + age + tcell
ret <- cfc.survreg(formul, bmt[1:300, ], bmt[-(1:300), ]
, Nmax = 300, rel.tol = 1e-3)
```

*CFC*version 1.2.0 Index]