Title: Competing Risks Regression for Stratified and Clustered Data
Version: 1.1.2
Author: Bingqing Zhou and Aurelien Latouche
Description: Extension of 'cmprsk' to Stratified and Clustered data. A goodness of fit test for Fine-Gray model is also provided. Methods are detailed in the following articles: Zhou et al. (2011) <doi:10.1111/j.1541-0420.2010.01493.x>, Zhou et al. (2012) <doi:10.1093/biostatistics/kxr032>, Zhou et al. (2013) <doi:10.1002/sim.5815>.
Depends: survival
Maintainer: Aurelien Latouche <aurelien.latouche@cnam.fr>
License: GPL-2
NeedsCompilation: yes
Packaged: 2022-06-10 15:45:02 UTC; al
Repository: CRAN
Date/Publication: 2022-06-10 21:20:20 UTC

Breast Cancer Data

Description

Data Randomly Generated According To El178 clinical trial

Usage

data(bce)

Format

A data frame with 200 observations and the following 6 variables.

trt

Treatment: 0=Placebo, 1=Tamoxifen

time

Event time

type

Event type. 0=censored, 1=Breast Cancer recurrence , 2=Death without recurrence

nnodes

Number of positive nodes

tsize

Tumor size

age

Age

Examples

data(bce)

Clustered competing risks simulated data

Description

sample of 200 observations

Usage

data(cdata)

Format

A data frame with 200 observations and the following 4 variables. Simulation is detailed on the paper Competing Risk Regression for clustered data. Zhou, Fine, Latouche, Labopin. 2011. In Press. Biostatistics.

ID

Id of cluster, each cluster is of size 2

ftime

Event time

fstatus

Event type. 0=censored, 1 , 2

z

a binary covariate with P(z=1)=0.5

Examples

data(cdata)

Multicenter Bone Marrow transplantation data

Description

Random sub sample of 400 patients

Usage

data(center)

Format

A data frame with 400 observations and the following 5 variables.

id

Id of transplantation center

ftime

Event time

fstatus

Event type. 0=censored, 1=Acute or Chronic GvHD , 2=Death free of GvHD

cells

source of stem cells: peripheral blood vs bone marrow

fm

female donor to male recipient match

Examples

data(center)

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.

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])

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.

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)

For internal use

Description

for internal use

Author(s)

Bingqing Zhou

Print method for crrs output

Description

Prints call for crrs object

Usage

## S3 method for class 'crrs'
print(x, ...)

Arguments

x

crr object (output from crrs())

...

additional arguments to print()

Author(s)

B. Zhou

Goodness-of-fit test for proportional subdistribution hazards model

Description

This Goodness-of-fit test proposed a modified weighted Schoenfeld residuals to test the proportionality of subdistribution hazards for the Fine and Gray model

Usage

psh.test(time, fstatus, z, D=c(1,1), tf=function(x) cbind(x,x^2), init)

Arguments

time

vector of failure times

fstatus

failure status =0 if censored

z

covariates

D

components of z that are tested for time-varying effect

tf

functions of t for z being tested on the same location

init

initial values of regression parameters (default=all 0)

Details

The proposed score test employs Schoenfeld residuals adapted to competing risks data. The form of the test is established assuming that the non-proportionality arises via time-dependent coefficients in the Fine-Gray model, similar to the test of Grambsch and Therneau.

Value

Returns a data.frame with percentage of cens, cause 1, Test Statistic, d.f. ,p-value

Author(s)

Bingqing Zhou, bingqing.zhou@yale.edu

References

Zhou B, Fine JP, Laird, G. (2013). Goodness-of-fit test for proportional subdistribution hazards mode. Statistics in Medicine. In Press.

Examples

data(bce)
attach(bce)
lognodes <- log(nnodes)
Z1 <- cbind(lognodes, tsize/10, age, trt)  
# trt = 0 if placebo, = 0 treatment
# testing for linear time varying effect of trt
psh.test(time=time, fstatus=type, z=Z1, D=c(0,0,0,1), tf=function(x) x)
# testing for quadratic time varying effect of trt
psh.test(time=time, fstatus=type, z=Z1, D=c(0,0,0,1), tf=function(x) x^2)
# testing for log time varying effect of trt
psh.test(time=time, fstatus=type, z=Z1, D=c(0,0,0,1), 
tf=function(x) log(x))
# testing for both linear and quadratic time varying effect of trt
psh.test(time=time, fstatus=type, z=Z1, 
D=matrix(c(0,0,0,1,0,0,0,1), 4,2),  tf=function(x) cbind(x,x^2))