| Title: | Competing Risks Regression for Stratified and Clustered Data |
|---|---|
| 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>. |
| Authors: | Bingqing Zhou and Aurelien Latouche |
| Maintainer: | Aurelien Latouche <[email protected]> |
| License: | GPL-2 |
| Version: | 1.1.2 |
| Built: | 2024-12-05 06:48:50 UTC |
| Source: | CRAN |
Data Randomly Generated According To El178 clinical trial
data(bce)data(bce)
A data frame with 200 observations and the following 6 variables.
trtTreatment: 0=Placebo, 1=Tamoxifen
timeEvent time
typeEvent type. 0=censored, 1=Breast Cancer recurrence , 2=Death without recurrence
nnodesNumber of positive nodes
tsizeTumor size
ageAge
data(bce)data(bce)
sample of 200 observations
data(cdata)data(cdata)
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.
IDId of cluster, each cluster is of size 2
ftimeEvent time
fstatusEvent type. 0=censored, 1 , 2
za binary covariate with P(z=1)=0.5
data(cdata)data(cdata)
Random sub sample of 400 patients
data(center)data(center)
A data frame with 400 observations and the following 5 variables.
idId of transplantation center
ftimeEvent time
fstatusEvent type. 0=censored, 1=Acute or Chronic GvHD , 2=Death free of GvHD
cellssource of stem cells: peripheral blood vs bone marrow
fmfemale donor to male recipient match
data(center)data(center)
Regression modeling of subdistribution hazards for clustered right censored data. Failure times within the same cluster are dependent.
crrc(ftime,fstatus,cov1,cov2,tf,cluster, cengroup,failcode=1, cencode=0, subset, na.action=na.omit, gtol=1e-6,maxiter=10,init)crrc(ftime,fstatus,cov1,cov2,tf,cluster, cengroup,failcode=1, cencode=0, subset, na.action=na.omit, gtol=1e-6,maxiter=10,init)
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 |
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 |
maxiter |
maximum number of iterations in Newton algorithm (0 computes
scores and var at |
init |
initial values of regression parameters (default=all 0) |
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.
Returns a list of class crr, with components
$coef |
the estimated regression coefficients |
$loglik |
log pseudo-liklihood evaluated at |
$score |
derivitives of the log pseudo-likelihood evaluated at |
$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 |
$loglik.null |
The value of the log pseudo-likelihood when all the coefficients are 0 |
Bingqing Zhou, [email protected]
Zhou B, Fine J, Latouche A, Labopin M.(2012). Competing Risks Regression for Clustered data. Biostatistics. 13 (3): 371-383.
cmprsk
#library(cmprsk) #crr(ftime=cdata$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])#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])
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
crrs(ftime, fstatus, cov1, cov2, strata, tf, failcode=1, cencode=0, ctype=1, subsets, na.action=na.omit, gtol=1e-6, maxiter=10,init)crrs(ftime, fstatus, cov1, cov2, strata, tf, failcode=1, cencode=0, ctype=1, subsets, na.action=na.omit, gtol=1e-6, maxiter=10,init)
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 |
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 |
maxiter |
maximum number of iterations in Newton algorithm (0 computes
scores and var at |
init |
initial values of regression parameters (default=all 0) |
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.
Returns a list of class crr, with components (see crr for details)
$coef |
the estimated regression coefficients |
$loglik |
log pseudo-liklihood evaluated at |
$score |
derivitives of the log pseudo-likelihood evaluated at |
$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 |
$loglik.null |
The value of the log pseudo-likelihood when all the coefficients are 0 |
Bingqing Zhou, [email protected]
Zhou B, Latouche A, Rocha V, Fine J. (2011). Competing Risks Regression for Stratified Data. Biometrics. 67(2):661-70.
cmprsk
## #using fine and gray model #crr(ftime=center$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)## #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)
Prints call for crrs object
## S3 method for class 'crrs' print(x, ...)## S3 method for class 'crrs' print(x, ...)
x |
crr object (output from |
... |
additional arguments to |
B. Zhou
This Goodness-of-fit test proposed a modified weighted Schoenfeld residuals to test the proportionality of subdistribution hazards for the Fine and Gray model
psh.test(time, fstatus, z, D=c(1,1), tf=function(x) cbind(x,x^2), init)psh.test(time, fstatus, z, D=c(1,1), tf=function(x) cbind(x,x^2), init)
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) |
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.
Returns a data.frame with percentage of cens, cause 1, Test Statistic, d.f. ,p-value
Bingqing Zhou, [email protected]
Zhou B, Fine JP, Laird, G. (2013). Goodness-of-fit test for proportional subdistribution hazards mode. Statistics in Medicine. In Press.
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))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))