Package {CopulaSCR}

CopulaSCR: Algorithms for fitting copula-based semi-competing risks models

Description

The package offers a comprehensive collection of practical and easy-to-use tools for analyzing semi-competing risks with single or multiple intermediate event times and a (possibly) correlated terminal event. The joint distribution of these event times are built upon an Archimedean copula structure. The implemented estimating procedure does not require any parametric assumption on the marginal distributions. The package also includes tools for visualization of semi-competing risks data and simulation from the models.

Author(s)

Maintainer: Tonghui Yu tonghui_yu@126.com

Authors:

• Binhui Zhang

References

Yu, Tonghui, and Xiang, Liming. Learning association from multiple intermediate events for dynamic prediction of survival: an application to cardiovascular disease prognosis,Biometrics, 2026, 82(2): ujag087.

Yu, Tonghui, Zhang, Binhui, Xiang, Liming, Ma, Jianwei, and Chen, Chixiang. CopulaSCR: An R Package for the Analysis of Semi-competing Risks Data Using Copula-based Models. Working paper.

Dependence Measures for Bivariate Copulas

Description

Compute Kendall's Tau of an Archimedean copula and copula parameter given the value of Kendall's Tau.

Usage

Caltau(copulafam, copulaparam)

Calitau(copulafam, tau)

Arguments

Value

A numeric value or numeric vector representing Kendall's tau for the specified copula family and association parameter.

See Also

tau

Examples

Caltau(copulafam = "frank", copulaparam = 2) # output: tau
Calitau(copulafam = "frank", tau = 0.5) # output: copulaparam

Example semi-competing risks dataset with a single intermediate event

Description

A simulated dataset for semi-competing risks analysis with a single intermediate event and one terminal event.

Usage

data(SCRdata)

Format

A data frame with 100 rows and 4 variables:

T1

Observed time for the intermediate event.

T2

Observed time for the terminal event.

event1

Event indicator for the intermediate event.

event2

Event indicator for the terminal event.

Example semi-competing risks dataset by treatment group

Description

A simulated dataset for semi-competing risks analysis with a single intermediate event, one terminal event, and a treatment variable.

Usage

data(SCRdata_by_tr)

Format

A data frame with 500 rows and 5 variables:

T1

Observed time for the intermediate event.

T2

Observed time for the terminal event.

event1

Event indicator for the intermediate event.

event2

Event indicator for the terminal event.

tr

Treatment group indicator.

Brier Scores for terminal survival prediction

Description

Brier Scores for terminal survival prediction

Usage

dyBS(surv, S_D, T2, event2, times=knots(S_D), reference = TRUE,
  tu=NULL, int.method=c("none","fmm","natural","periodic","monoH.FC","hyman"))

Arguments

Value

A list containing the following components: BS=data.frame(time=times,BS=BS), IBS=IBS

BS

Estimated Brier Score Curve.

IBS

Integrated Brier Score over the range of evaluation times.

See Also

mscr, predict.mscr

Examples

set.seed(12345)
data<- simSCRmul(n=100,copulafam = "frank",K=3,
                 tau.alpha = .5,tau.theta=.5)

fit<- mscr(mT1=data$T1,T2=data$T2,mevent1=data$event1,event2=data$event2,
           copulafam = "frank")
mT1.te<- data$T1
mT1.te[data$event1==0]<- NA

####  dynamic prediction
surv<- predict(fit, t1obs=mT1.te,type="surv")

## naive Pr(D>t|D> t_m)
surv0<- predict(fit, t1obs=mT1.te,type="surv",cause=0)

## Pr(D>t|T_1=t_1,D> t_1)
surv1<- predict(fit, t1obs=mT1.te,type="surv",cause=1,maxobs = FALSE)

## Pr(D>t|T_1=t_1,D> t_m)
surv1m<- predict(fit, t1obs=mT1.te,type="surv",cause=1,maxobs = TRUE)

### Brier Scores
BS<- dyBS(surv,S_D = fit$S_D,T2=data$T2,event2=data$event2,int.method = "natural")
BS0<- dyBS(surv0,S_D = fit$S_D,T2=data$T2,event2=data$event2,int.method = "natural")
BS1<- dyBS(surv1,S_D = fit$S_D,T2=data$T2,event2=data$event2,int.method = "natural")
BS1m<- dyBS(surv1m,S_D = fit$S_D,T2=data$T2,event2=data$event2,int.method = "natural")
(ibs<-c(BS$IBS,BS0$IBS,BS1$IBS,BS1m$IBS)) ## integrated BS

### plot Brier Scores
plot(BS$BS,ylim = c(0,0.7),type="l",xlab="time",ylab="Brier Score",col=1)
lines(BS0$BS,col=2)
lines(BS1$BS,col=3)
lines(BS1m$BS,col=4)
legend("topright",col = 1:4,lty = 1,legend = c("DP","P0","P1","P1m"))

Example semi-competing risks dataset with multiple intermediate events

Description

A simulated dataset for semi-competing risks analysis with three intermediate events and one terminal event.

Usage

data(mSCRdata)

Format

An object of class mscrData, implemented as a list with five components:

T1

A numeric matrix with 100 rows and 3 columns, containing observed times for three intermediate events.

T2

A numeric vector of length 100, containing observed terminal event times.

event1

A numeric matrix with 100 rows and 3 columns, containing event indicators for the three intermediate events.

event2

A numeric vector of length 100, containing terminal event indicators.

call

The function call used to generate the data.

Association analysis for semi-competing risks data with multiple intermediate event times

Description

Fits a copula-based model for semi-competing risks data with multiple intermediate event times. The function first estimates the marginal survival curves and pairwise association parameters for each intermediate event and the terminal event, and then estimates the dependence parameter among multiple intermediate event times.

Usage

mscr(mT1,T2,mevent1,event2, 
            copulafam=c("frank","clayton","joe","gumbel","amh"), 
            nsim=500,ncore= 1,tol=0.01,a=NULL,b = NULL, 
            positive=TRUE, msurv.method= "JFKC",lower=0.02,upper=0.96)

Arguments

Value

An object of class "mscr" representing the fitted model. The returned object is a list containing at least the following components:

alpha

Estimated copula parameter for the dependence among multiple intermediate event times.

logLik.alpha

Pseudo-log-likelihood evaluated at alpha.

alpha.int

Lower and upper bounds used in the search interval for alpha.

call

The matched function call.

copulafam

The specified Archimedean copula family.

theta

Estimated copula parameters for the association between each intermediate event and the terminal event.

tau.alpha

Estimated Kendall's tau corresponding to alpha.

tau.theta

Estimated Kendall's tau values corresponding to theta.

S_D

Estimated survival function of the terminal event time.

S_k

A list of estimated survival functions for the intermediate event times.

mar.fits

A list of fitted scrsurv objects for the marginal analyses.

References

Yu, Tonghui, and Xiang, Liming (2024). Association analysis of multiple intermediate events and dynamic terminal prediction. Working paper.

Yu, Tonghui, Zhang, Binhui, Xiang, Liming, Ma, Jianwei, and Chen, Chixiang. CopulaSCR: An R Package for the Analysis of Semi-competing Risks Data Using Copula-based Models. Working paper.

See Also

plot.mscr

Examples

set.seed(12345)
data <- simSCRmul(
  n = 100, copulafam = "frank", K = 3,
  tau.alpha = 0.5, tau.theta = 0.5
)

fit <- mscr(
  mT1 = data$T1,
  T2 = data$T2,
  mevent1 = data$event1,
  event2 = data$event2,
  copulafam = "frank",
  ncore = 1
)

print(fit)
plot(fit$mar.fits[[1]])
plot(fit, type = "msurv")
plot(fit, type = "data")

Plot of 'mscr' object

Description

Plot of 'mscr' object

Usage

## S3 method for class 'mscr'
plot(
  x,
  type = c("msurv", "data"),
  nt.seperate = FALSE,
  linetype = "strata",
  censor = FALSE,
  xlab = "Time",
  ylab = "Survival probability",
  fontsize = 10,
  height = 7,
  ...
)

Arguments

Value

No return value.

See Also

mscr

Plot of marginal survival curves of nonterminal and terminal event times

Description

Plot of marginal survival curves of nonterminal and terminal event times

Usage

## S3 method for class 'scrsurv'
plot(
  x,
  linetype = "strata",
  conf.int = FALSE,
  censor = FALSE,
  xlab = "Time",
  ylab = "Survival probability",
  align = "h",
  ...
)

Arguments

Value

No return value.

See Also

scrsurv

Dynamic terminal survival prediction

Description

Fitting semi-competing risks data with multiple intermediate event times using a copula-based model. Survival prediction for terminal event is dynamically estimated based on the updated observed intermediate event times.

Usage

## S3 method for class 'mscr'
predict(
  object,
  t1obs,
  times = knots(object$S_D),
  cause = NULL,
  type = c("msurv", "surv", "rrms", "rmst", "qrl", "qst"),
  tu = max(knots(object$S_D)),
  tau = 0.5,
  nsim = 1000,
  maxobs = TRUE,
  ...
)

Arguments

Value

A list containing prediction results. The returned components depend on type, including predicted survival probabilities, restricted residual mean survival time, restricted mean survival time, quantile residual lifetime, or quantile survival time.

See Also

mscr

Examples

set.seed(12345)
data<- simSCRmul(n=100,copulafam = "frank",K=3,
                 tau.alpha = .5,tau.theta=.5)

fit<- mscr(mT1=data$T1,T2=data$T2,mevent1=data$event1,event2=data$event2,
           copulafam = "frank",ncore = 1)
mT1<- data$T1
mT1[data$event1==0]<- NA
mT1.te<- mT1[1:4,]
## estimated margianl survival function for each nonterminal event time
msurv<- predict(fit, t1obs=mT1.te,type="msurv")

####  dynamic prediction
surv<- predict(fit, t1obs=mT1.te,type="surv")
rmst<- predict(fit, t1obs=mT1.te,type="rmst")
rmst<- sapply(rmst$rmst, function(x)x$rmst)

## naive Pr(D>t|D> t_m)
surv0<- predict(fit, t1obs=mT1.te,type="surv",cause=0)
rmst0<- predict(fit, t1obs=mT1.te,type="rmst",cause=0)
rmst0<- sapply(rmst0$rmst, function(x)x$rmst)

## Pr(D>t|T_1=t_1,D> t_1)
surv1<- predict(fit, t1obs=mT1.te,type="surv",cause=1,maxobs = FALSE)
rmst1<- predict(fit, t1obs=mT1.te,type="rmst",cause=1,maxobs = FALSE)
rmst1<- sapply(rmst1$rmst, function(x)x$rmst)

## Pr(D>t|T_1=t_1,D> t_m)
surv1m<- predict(fit, t1obs=mT1.te,type="surv",cause=1,maxobs = TRUE)
rmst1m<- predict(fit, t1obs=mT1.te,type="rmst",cause=1,maxobs = TRUE)
rmst1m<- sapply(rmst1m$rmst, function(x)x$rmst)

## compare with the true death time
T2<- data$T2
T2[data$event2==0]<- NA
T2<- T2[1:4]
## mean square error
apply(abs(cbind(T2-rmst,T2-rmst0,T2-rmst1,T2-rmst1m)^2),2,mean)

Survival prediction

Description

Estimation of marginal survival functions, joint survival function, cross-hazard ratio function and conditional survival function under semi-competing risks data with single intermediate event.

Usage

## S3 method for class 'scrsurv'
predict(
  object,
  newdata,
  type = c("msurv", "jsurv", "csurv", "chr"),
  t1,
  t2,
  s2,
  t1equal = FALSE,
  bystrata = TRUE,
  ...
)

Arguments

Value

A list with components: t1, t2, and one of jsurv, msurv, csurv, and chr.

See Also

scrassonp, scrsurv, predictscr

Examples

library(CopulaSCR)
set.seed(12345)
simdata<- simSCR(n = 50,tau = 0.5, copulafam = "frank")
fitasso<- scrassonp(t1.formula=Surv(T1, event1) ~ 1,
                    t2.formula=Surv(T2, event2) ~ 1,
                    data = simdata,copulafam="frank",
                    a=quantile(simdata$T1,0.9),
                    b=quantile(simdata$T2,0.9),B=10,seed = 12345,
                    se = TRUE,se.method = "resampling")
summary(fitasso)
fit<- scrsurv(fit = fitasso, method= "JFKC",surv2km=FALSE,B=fitasso$B,
se.method = fitasso$se.method,conf.int=TRUE,conftype =3)

t1<- t2<- seq(0,3.5,0.1)
### marginal survival probabilities
ms<- predict(fit,t1=t1, type="msurv")

### joint survival probabilities
js<- predict(fit,t1=t1, t2=t2,type="jsurv")

## stratification analysis
set.seed(12345)
simdata2<- simSCRtr(n = 100,K=3, tau = c(0.3,0.5,0.6), copulafam = "frank",
                    params=list(marginsDist = rep("exp",2), rate1=c(0.5,1,1.2),rate2=1))
fitasso<- scrassonp(t1.formula=Surv(T1, event1) ~ tr,
                    t2.formula=Surv(T2, event2) ~ tr,
                    data=simdata2, copulafam="frank",a=quantile(simdata2$T1,0.9),
                    b=quantile(simdata2$T2,0.9),B=0)
fittr<- scrsurv(fit = fitasso, method= "JFKC",surv2km=FALSE,B=0)
t1<- s2<- c(0.2,0.5,1)
t2<-  seq(0,3.5,0.1)
ms2<- predict(fittr,t1=t1, type="msurv",newdata=simdata2[1:3,])
js2<- predict(fittr,t1=t1, t2=t2, type="jsurv",newdata=simdata2[1:3,])
cs2<- predict(fittr,t1=t1, t2=t2,s2=s2, type="csurv",newdata=simdata2[1:3,])
chr2<- predict(fittr,t1=t1, t2=t2, type="chr",newdata=simdata2[1:3,])

Survival prediction

Description

Estimation of joint survival function, cross-hazard ratio function and conditional survival function under semi-competing risks data with single intermediate event.

Usage

predictscr(data, fit, t1.formula, t2.formula, type = c("jsurv","csurv","chr"),
   t1, t2, s2, t1equal = FALSE, method = c("np", "sp"),...)

predjsurv(data, fit, t1.formula, t2.formula, t1, t2, method = c("np", "sp"),newdata)

predchr(data,fit,t1.formula, t2.formula, t1, t2, method=c("np","sp"),
   tau, copulafam = c("clayton","frank","joe","gumbel","amh"),newdata)

predcsurv(data, fit, t1.formula, t2.formula, t1, t2, s2,
  t1equal = FALSE, method = c("np","sp"), newdata)

Arguments

Value

jsurv

A matrix for joint survival function Pr(\tilde{T}_1 > t_1, \tilde{T}_2 > t_2), where \tilde{T}_1, and \tilde{T}_2 are the true nonterminal and terminal event times, respectively.

chr

A matrix for cross-hazard ratio function.

csurv

A matrix for conditional survival function Pr(\tilde{T}_2 > t_2|\tilde{T}_1 > t_1, \tilde{T}_2 > s_2) if t1equal = FALSE and Pr(\tilde{T}_2 > t_2|\tilde{T}_1 = t_1, \tilde{T}_2 > s_2) if t1equal = TRUE.

See Also

scrassonp, scrsurv,predict.scrsurv

Examples

library(CopulaSCR)
set.seed(12345)
simdata<- simSCR(n = 50,tau = 0.5, copulafam = "frank")
fitasso<- scrassonp(t1.formula=Surv(T1, event1) ~ 1,
                    t2.formula=Surv(T2, event2) ~ 1,
                    data = simdata,copulafam="frank",
                    a=quantile(simdata$T1,0.9),
                    b=quantile(simdata$T2,0.9),B=10,seed = 12345,
                    se = TRUE,se.method = "resampling")
summary(fitasso)
fit22<- scrsurv(fit = fitasso, method= "JFKC",surv2km=FALSE,B=fitasso$B,
se.method = fitasso$se.method,conf.int=TRUE,conftype =3)

t1<- t2<- seq(0,3.5,0.1)
### joint survival probabilities
js<- predjsurv(fit=fit22,t1=t1, t2=t2,method="sp")
# same with
js2<- predictscr(fit=fit22,t1=t1, t2=t2,method="sp",type="jsurv")
# or semiparametric estimator
# js2<- predict(fit=fit22,t1=t1, t2=t2,type="jsurv")

### cross-hazard ratio function
chr<- predchr(fit=fit22, t1=t1, t2=t2,method="sp")
# same with
chr2<- predictscr(fit=fit22,t1=t1, t2=t2,method="sp",type="chr")
# or semiparametric estimator
# chr2<- predict(fit=fit22,t1=t1, t2=t2,type="chr")


## plot jsurv and chr
library(graphics)
filled.contour(x=t1,y=t2,z=do.call(rbind,js$jsurv),nlevels = 30,
xlab="t1",ylab="t2",main = "joint survival function",
plot.axes = {axis(1, seq(0,3.5,0.5))
  axis(2, seq(0,3.5,0.5))
    contour(x = t1,y=t2,do.call(rbind,js$jsurv),
    add = TRUE, lwd = 2)})


filled.contour(x=t1,y=t2,z=do.call(rbind,chr$chr),nlevels = 30,
xlab="t1",ylab="t2",main = "cross-hazard ratio",
plot.axes = {axis(1, seq(0,3.5,0.5))
 axis(2, seq(0,3.5,0.5))
 contour(x = t1,y=t2,do.call(rbind,chr$chr),
 add = TRUE, lwd = 2)})

# conditional survival probabilities
t1<- s2<- c(0.2,0.5,1)
t2<-  seq(0,3.5,0.1)
cs<- predcsurv(fit=fit22,t1=t1, t2=t2, s2=s2, t1equal =TRUE,method="sp")$csurv
# same with
cs2<- predictscr(fit=fit22,t1=t1, t2=t2, s2=s2, t1equal =TRUE,
 method="sp",type="csurv")$csurv
# or semiparametric estimator
# cs2<- predict(fit=fit22,t1=t1, t2=t2,s2=s2, t1equal =TRUE,type="csurv")$csurv

## plot csurv
cs<- lapply(seq(length(cs)),function(k)cs[[k]][,k])
plot(c(0,max(t2)),c(0,1),type="n",xlab = "t2", ylab="",
  main ="conditional survival given T1=t1, T2>t1")
for (k in 1:length(cs)) {lines(t2,cs[[k]],col=k)}
legend(x="topright",legend = paste0("t1=",t1),col = 1:length(cs),lty=1)

Print objects in the mscr library

Description

Pretty printing of objects created with the functionality of the ‘scrassonp’ library.

Usage

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

Arguments

Value

No return value.

See Also

mscr, scrsurv

Print objects in the scrassonp library

Description

Pretty printing of objects created with the functionality of the ‘scrassonp’ library.

Usage

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

Arguments

Value

No return value.

See Also

scrassonp, scrsurv

Print objects in the scrsurv library

Description

Pretty printing of objects created with the functionality of the ‘scrsurv’ library.

Usage

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

Arguments

Value

No return value.

See Also

scrsurv, scrassonp, plot.scrsurv, print.scrassonp

Concordance estimator for association parameter

Description

Fitting a semiparametric copula-based model with pre-specified Archimedean copula. The copula parameter is solved from a generalized concordance estimating equations proposed by Lakhal et al. (2008).

Usage

scrassonp(t1.formula, t2.formula, data = parent.frame(),
     equalweight = FALSE, model = TRUE, a = 0, b = 0,positive = TRUE, tol = 1e-5,
     copulafam = c("clayton", "frank", "joe", "gumbel", "amh"), se = FALSE,
     se.method = c("bootstrap", "resampling"), B = 0, seed = NULL)

Arguments

Details

See details in Lakhal et al. (2008) and Yu et al. (2026)'s supplementary materials.

Value

An object of class "scrassonp" representing the fit. The scrassonp object is a list containing at least the following components:

Call

An object of class call.

tau

Estimated Kendall's tau corresponding to estimated copula parameter.

copulaparam

Estimated copula parameter in the Archimedean copula.

copulafam

Prespecified Archimedean copula.

zetas

Resampling replicates.

param.boot

Estimated copula parameter under each set of resampling/Bootstrap replicates.

param.se

SE for estimated copula parameter.

tau.boot

Estimated Kendall's tau under each set of resampling/Bootstrap replicates.

tau.se

SE for estimated Kendall's tau.

...

The object will also contain the input arguments.

References

Fine, J. P., Jiang, H., and Chappell, R. (2001). On semi-competing risks data. Biometrika, 88(4):907–919.

Lakhal, L., Rivest, L.-P., and Abdous, B. (2008). Estimating survival and association in a semicompeting risks model. Biometrics, 64(1):180–188.

Yu, Tonghui, Zhang, Binhui, Xiang, Liming, Ma, Jianwei, and Chen, Chixiang. CopulaSCR: An R Package for the Analysis of Semi-competing Risks Data Using Copula-based Models. Working paper.

See Also

scrsurv

Examples

set.seed(12345)
simdata<- simSCR(n = 100,tau = 0.5, copulafam = "frank")
fitasso<- scrassonp(t1.formula=Surv(T1, event1) ~ 1,
                    t2.formula=Surv(T2, event2) ~ 1,
                    data = simdata, copulafam="frank",
                    a=quantile(simdata$T1,0.9), b=quantile(simdata$T2,0.9),
                    B=20,seed = 12345,se = TRUE,se.method = "resampling")
c(tau.est = fitasso$tau, tau.se = fitasso$tau.se)
# tau.est = 0.5216944 tau.se =  0.0891336

set.seed(12345)
simdata<- simSCRtr(n = 100,K=3, tau = c(0.3,0.5,0.6), copulafam = "frank",
                   params=list(marginsDist = rep("exp",2),
                               rate1=c(0.5,1,1.2),rate2=1))
fitasso<- scrassonp(t1.formula=Surv(T1, event1) ~ tr,
                    t2.formula=Surv(T2, event2) ~ tr,
                    data=simdata, copulafam="frank",
                    a=quantile(simdata$T1,0.9), b=quantile(simdata$T2,0.9),
                    B=20,seed = 12345,se = TRUE,se.method = "resampling")
c(tau.est = round(fitasso$tau, 2), tau.se = round(fitasso$tau.se, 2))
# tau.est = 0.39 0.51 0.69 tau.se =  0.38 0.14 0.1

Nonparametric estimation of marginal survival functions

Description

Fitting a semiparametric copula-based model with pre-specified Archimedean copula. Plugging estimated copula parameter solving from a generalized concordance estimating equations proposed by Lakhal et al. (2008), the marginal survival function of nonterminal event time can be estimated using one of three available methods: "FJC" for Fine et al. (2001)'s estimator, "JFKC" for Jiang et al. (2005)'s estimator, and "LRA" for Lakhal et al. (2008)'s estimator. Or alternatively, a Kernel-based estimator ("Ker") is also provided. The marginal survival function of terminal event time can be estimated using one of two available methods in: "JFKC" for Jiang et al. (2005)'s estimator, and the classical Kaplan-Meier estimator (see survival package).

Usage

scrsurv(fit, t1.formula, t2.formula, data = parent.frame(), tau,
  copulafam = c("clayton", "frank", "joe", "gumbel", "amh"),
  method = c("JFKC", "LRA", "FJC", "Ker"), surv2km = TRUE,
  B = 100, se.method = c("bootstrap","resampling"), conf.int = TRUE,
  conftype = 1, seed = NULL)

Arguments

Value

An object of class "scrsurv" representing the fit. The scrsurv object is a list containing at least the following components:

t1.surv

A list containing "time", "n.risk", "n.event", "n.censor", "surv", "cumhaz", "type", "std.err", "std.chaz", "lower", "upper" (See survfit.object() in survival package).

t2.surv

An object of class "list" if surv2km = FALSE or class "survfit" if surv2km = TRUE (See survfit.object() in survival package).

copulaparam

Copula parameter in the Archimedean copula.

copulafam

Prespecified Archimedean copula structure.

tau

Kendall's tau corresponding to copula parameter.

Call

An object of class call.

copulaparams

Estimated copula parameter under each set of resampling/Bootstrap replicates.

t1data

The data for nonterminal event times of class "list". It includes observed times ("time"), censoring indicators ("event"), covariates ("X")

t2data

The data for terminal event times of class "list".

...

References

Fine, J. P., Jiang, H., and Chappell, R. (2001). On semi-competing risks data. Biometrika, 88(4):907–919.

Jiang, H., Fine, J. P., Kosorok, M. R., and Chappell, R. (2005). Pseudo self-consistent estimation of a copula model with informative censoring. Scandinavian Journal of Statistics, 32(1):1–20.

Lakhal, L., Rivest, L.-P., and Abdous, B. (2008). Estimating survival and association in a semicompeting risks model. Biometrics, 64(1):180–188.

Nevo D, Gorfine M (2022). Causal inference for semi-competing risks data. Biostatistics, 23(4), 1115–1132.

Yu, Tonghui, Zhang, Binhui, Xiang, Liming, Ma, Jianwei, and Chen, Chixiang. CopulaSCR: An R Package for the Analysis of Semi-competing Risks Data Using Copula-based Models. Working paper.

See Also

scrassonp

Examples

set.seed(12345)
simdata<- simSCR(n = 50,tau = 0.5, copulafam = "frank")
fitasso<- scrassonp(t1.formula=Surv(T1, event1) ~ 1,
         t2.formula=Surv(T2, event2) ~ 1,
         data = simdata, copulafam="frank",
         a=quantile(simdata$T1,0.9),
         b=quantile(simdata$T2,0.9),B=10,seed = 12345,
         se = TRUE,se.method = "resampling")


c(tau.est = fitasso$tau, tau.se = fitasso$tau.se)
fit21<- scrsurv(fit = fitasso, method= "JFKC",surv2km=FALSE,B=10,
se.method = "resampling",conf.int=TRUE,conftype =1,seed = 12345)
plot(fit21,conf.int = TRUE)
fit22<- scrsurv(fit = fitasso, method= "JFKC",surv2km=FALSE,B=fitasso$B,
se.method = fitasso$se.method,conf.int=TRUE,conftype =3)
plot(fit22,conf.int = TRUE)

#####  estimation of nonparametric marginals with specified Kendall's tau
fit1<- scrsurv(t1.formula=Surv(T1, event1) ~ 1, t2.formula=Surv(T2, event2) ~ 1,
data =simdata, tau=0.5,copulafam="frank", B=10,
method= "FJC",se.method = "bootstrap",conf.int=TRUE,seed = 12345)
plot(fit1,conf.int = TRUE)


set.seed(12345)
simdata2<- simSCRtr(n = 500,K=3, tau = c(0.3,0.5,0.6), copulafam = "frank",
                    params=list(marginsDist = rep("exp",2), rate1=c(0.5,1,1.2),rate2=1))
fitasso<- scrassonp(t1.formula=Surv(T1, event1) ~ tr,
                    t2.formula=Surv(T2, event2) ~ tr,
                    data=simdata2, copulafam="frank",
                    a=quantile(simdata2$T1,0.9), b=quantile(simdata2$T2,0.9),
                    B=10,seed = 12345,se = TRUE,se.method = "resampling")
c(tau.est = round(fitasso$tau, 2), tau.se = round(fitasso$tau.se, 2))

fit21tr<- scrsurv(fit = fitasso, method= "JFKC",surv2km=FALSE,B=10,seed = 12345,
                  se.method = "resampling",conf.int=TRUE,conftype =1)
fit21tr
plot(fit21tr,conf.int=TRUE)

Simulating a semi-competing risks data from a copula-based parametric model

Description

Simulating a semi-competing risks data from a copula-based parametric model with the Archimedean copula structure.

Usage

simSCR(n  = 100, cens.rate = 5, copulafam = "frank", tau = 0.5,
params = list(marginsDist = rep("exp", 2),rate1 = 1,rate2 = 1))

Arguments

Value

A data.frame containing semi-competing risks outcomes from n subjects. It is of dimension n\times 4 with the columns named as T1, T2, event1 and event2.

T1

a vector of the observed nonterminal event times for the n individuals, i.e., \min(\tilde{T}_1,\tilde{T}_2,C), where \tilde{T}_1, and \tilde{T}_2 are the true nonterminal and terminal event times, respectively.

T2

a vector of the observed terminal event times for the n individuals, i.e., \tilde{T}_2,C).

event1

a vector of censoring indicators for the non-terminal event time (1=event occurred, 0=censored).

event2

a vector of censoring indicators for the terminal event time (1=event occurred, 0=censored).

See Also

scrassonp, scrsurv

Simulating a semi-competing risks data with multiple intermediate events

Description

Simulating a semi-competing risks data with multiple intermediate events from a copula-based parametric model with the Archimedean copula structure.

Usage

simSCRmul(n  = 100, cens.rate = 20, K = 7, copulafam = "frank",
  tau.alpha = 0.5, tau.theta = seq(0.85, 0.2,length.out = K),
  params = list(rate1 = 1, rate2 = 0.6),trueout=FALSE)

Arguments

Value

A data frame or list containing the simulated semi-competing risks data.

See Also

mscr

Simulating a semi-competing risks data from a copula-based parametric model

Description

Simulating a stratified semi-competing risks data from a copula-based parametric model with the Archimedean copula structure.

Usage

simSCRtr(n  = 100, K = 2, cens.rate = 5, copulafam = "frank", tau = 0.5,
params = list(marginsDist = rep("exp", 2),rate1 = 1,rate2 = 1))

Arguments

Value

A data.frame containing semi-competing risks outcomes from n subjects. It is of dimension n\times 5 with the columns named as T1, T2, event1, event2 and tr.

T1

a vector of the observed nonterminal event times for the n individuals, i.e., \min(\tilde{T}_1,\tilde{T}_2,C), where \tilde{T}_1, and \tilde{T}_2 are the true nonterminal and terminal event times, respectively.

T2

a vector of the observed terminal event times for the n individuals, i.e., \tilde{T}_2,C).

event1

a vector of censoring indicators for the non-terminal event time (1=event occurred, 0=censored).

event2

a vector of censoring indicators for the terminal event time (1=event occurred, 0=censored).

tr

treatment indicator.

See Also

scrassonp, scrsurv

Summary method for scrassonp objects.

Description

Summarizing the result of the association analysis in the semi-competing risks data. Confidence intervals are displayed when they are part of the fitted object.

Usage

## S3 method for class 'scrassonp'
summary(object, conf.int = TRUE, ...)

Arguments

Value

A data.frame with the relevant information.

See Also

scrassonp