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 tk, the model includes the term cov2[,j]*tf(tk)[,j] as a covariate. 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 < 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

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

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])


[Package crrSC version 1.1.2 Index]