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 tk, the model includes the term cov2[,j]*tf(tk)[,j] as a covariate. 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 < gtol maxiter maximum number of iterations in Newton algorithm (0 computes scores and var at init, but performs no iterations) 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 coef $score derivitives of the log pseudo-likelihood evaluated at coef $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.

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)



[Package crrSC version 1.1.2 Index]