crrc: Competing Risks Regression for Clustered Data

In crrSC: Competing Risks Regression for Stratified and Clustered Data

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, [e-mail]

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 documentation built on June 11, 2022, 1:05 a.m.