surv.factorial: Factorial survival analysis under dependent censoring

In compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival, Including Copula-Based Analyses for Dependent Censoring

Factorial survival analysis under dependent censoring

Description

Perform factorial survival analysis under dependent censoring under an assumed copula (Emura et al. 2024).

Usage

surv.factorial(t.vec,d.vec,group,copula,alpha,R=1000,t.upper=min(tapply(t.vec,group,max)),
C=NULL,S.plot=TRUE,mark.time=FALSE)

Arguments

t.vec	Vector of survival times (time to either death or censoring)
d.vec	Vector of censoring indicators, 1=death, 0=censoring
group	Vector of group indicators, 1, 2, ..., d
copula	Copula function: "CG.Clayton","CG.Gumbel" or "CG.Frank"
alpha	Copula parameter
R	The number of Monte Carlo simulations to find the critical value of the F-test
t.upper	Follow-up end (default is max(t.vec))
C	Contrast matrix
S.plot	If TRUE, the survival curve is displayed
mark.time	If TRUE, then curves are marked at each censoring time

Details

Estimates of treatment effects and the test results are shown.

Value

copula.parameter	Copula parameter
p	Estimates of treatment effects
Var	Variance estimates
F	F-statistic
c.simu	Critical value via the simulation method
c.anal	Critical value via the analytical method
P.value	P-value of the F-test

Author(s)

Takeshi Emura

References

Emura T, Ditzhaus M, Dobler D, Murotani K (2024), Factorial survival analysis for treatment effects under dependent censoring, Stat Methods Med Res 33(1):61-79.

Examples

library(survival)
data(cancer)
dat=subset(colon,etype==1)
## Treatment effects ##
t.vec=dat$time
d.vec=dat$status
trt=dat$rx
C12=matrix(c(1,-1,0),1,3,byrow=TRUE)
C13=matrix(c(1,0,-1),1,3,byrow=TRUE)
C23=matrix(c(0,1,-1),1,3,byrow=TRUE)
group=as.numeric(trt) # 1=Obs; 2=Lev; 3=Lev+5FU
# surv.factorial(t.vec,d.vec,group,alpha=2,copula=CG.Clayton)
# surv.factorial(t.vec,d.vec,group,alpha=2,copula=CG.Clayton,C=C12)
# surv.factorial(t.vec,d.vec,group,alpha=2,copula=CG.Clayton,C=C13)
# surv.factorial(t.vec,d.vec,group,alpha=2,copula=CG.Clayton,C=C23)
# alpha is a copula parameter

compound.Cox documentation built on April 3, 2025, 9:07 p.m.