crrc {crrSC}

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])