crrs {crrSC} | R Documentation |

## Competing Risks Regression for Stratified Data

### Description

Regression modeling of subdistribution hazards for stratified right censored data

Two types of stratification are addressed : Regularly stratified: small number of large groups (strata) of subjects Highly stratified: large number of small groups (strata) of subjects

### Usage

```
crrs(ftime, fstatus, cov1, cov2, strata,
tf, failcode=1, cencode=0,
ctype=1,
subsets, na.action=na.omit,
gtol=1e-6, maxiter=10,init)
```

### Arguments

`strata` |
stratification covariate |

`ctype` |
1 if estimating censoring dist within strata (regular stratification), 2 if estimating censoring dist across strata (highly stratification) |

`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 |

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

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

`subsets` |
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

Fits the stratified extension of the Fine and Gray model (2011). 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 (see crr for details)

`$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, Latouche A, Rocha V, Fine J. (2011). Competing Risks Regression for Stratified Data. Biometrics. 67(2):661-70.

### See Also

cmprsk

### Examples

```
##
#using fine and gray model
#crr(ftime=center$ftime, fstatus=center$fstatus,
#cov1=cbind(center$fm,center$cells))
#
# High Stratification: ctype=2
# Random sub-sample
data(center)
cov.test<-cbind(center$fm,center$cells)
crrs(ftime=center[,1],fstatus=center[,2],
cov1=cov.test,
strata=center$id,ctype=2)
```

*crrSC*version 1.1.2 Index]