crrc {crrSC} | R Documentation |

## Competing Risks Regression for Clustered Data

### Description

Regression modeling of subdistribution hazards for clustered right censored data. Failure times within the same cluster are dependent.

### Usage

```
crrc(ftime,fstatus,cov1,cov2,tf,cluster,
cengroup,failcode=1,
cencode=0, subset,
na.action=na.omit,
gtol=1e-6,maxiter=10,init)
```

### Arguments

`cluster` |
Clustering covariate |

`ftime` |
vector of failure/censoring times |

`fstatus` |
vector with a unique code for each failure type and a separate code for censored observations |

`cov1` |
matrix (nobs x ncovs) of fixed covariates (either cov1, cov2, or both are required) |

`cov2` |
matrix of covariates that will be multiplied by functions of time; if used, often these covariates would also appear in cov1 to give a prop hazards effect plus a time interaction |

`tf` |
functions of time. A function that takes a vector of times as
an argument and returns a matrix whose jth column is the value of
the time function corresponding to the jth column of cov2 evaluated
at the input time vector. At time |

`cengroup` |
vector with different values for each group with a distinct censoring distribution (the censoring distribution is estimated separately within these groups). All data in one group, if missing. |

`failcode` |
code of fstatus that denotes the failure type of interest |

`cencode` |
code of fstatus that denotes censored observations |

`subset` |
a logical vector specifying a subset of cases to include in the analysis |

`na.action` |
a function specifying the action to take for any cases missing any of ftime, fstatus, cov1, cov2, cengroup, or subset. |

`gtol` |
iteration stops when a function of the gradient is |

`maxiter` |
maximum number of iterations in Newton algorithm (0 computes
scores and var at |

`init` |
initial values of regression parameters (default=all 0) |

### Details

This method extends Fine-Gray proportional hazards model for subdistribution (1999) to accommodate situations where the failure times within a cluster might be correlated since the study subjects from the same cluster share common factors This model directly assesses the effect of covariates on the subdistribution of a particular type of failure in a competing risks setting.

### Value

Returns a list of class crr, with components

`$coef` |
the estimated regression coefficients |

`$loglik` |
log pseudo-liklihood evaluated at |

`$score` |
derivitives of the log pseudo-likelihood evaluated at |

`$inf` |
-second derivatives of the log pseudo-likelihood |

`$var` |
estimated variance covariance matrix of coef |

`$res` |
matrix of residuals |

`$uftime` |
vector of unique failure times |

`$bfitj` |
jumps in the Breslow-type estimate of the underlying sub-distribution cumulative hazard (used by predict.crr()) |

`$tfs` |
the tfs matrix (output of tf(), if used) |

`$converged` |
TRUE if the iterative algorithm converged |

`$call` |
The call to crr |

`$n` |
The number of observations used in fitting the model |

`$n.missing` |
The number of observations removed from the input data due to missing values |

`$loglik.null` |
The value of the log pseudo-likelihood when all the coefficients are 0 |

### Author(s)

Bingqing Zhou, bingqing.zhou@yale.edu

### References

Zhou B, Fine J, Latouche A, Labopin M.(2012). Competing Risks Regression for Clustered data. Biostatistics. 13 (3): 371-383.

### See Also

cmprsk

### Examples

```
#library(cmprsk)
#crr(ftime=cdata$ftime, fstatus=cdata$fstatus, cov1=cdata$z)
# Simulated clustered data set
data(cdata)
crrc(ftime=cdata[,1],fstatus=cdata[,2],
cov1=cdata[,3],
cluster=cdata[,4])
```

*crrSC*version 1.1.2 Index]