crrs {crrSC}

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, [e-mail]

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)