CG.test: Testing survival difference of two groups via the CG...

In compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival

Testing survival difference of two groups via the CG estimators

Description

Testing survival difference of two prognostic groups separated by a prognostic index (PI). Survival probabilities are computed by the CG estimators (Yeh, et al. 2023).

Usage

CG.test(t.vec,d.vec,PI,cutoff=median(PI),alpha=2,
copula=CG.Clayton,S.plot=TRUE,N=10000,mark.time=TRUE)

Arguments

t.vec	Vector of survival times (time to either death or censoring)
d.vec	Vector of censoring indicators, 1=death, 0=censoring
PI	Vector of real numbers (the values of a prognostic index)
cutoff	A number determining the cut-off value of a prognostic index
alpha	Copula parameter
copula	Copula function: "CG.Clayton","CG.Gumbel" or "CG.Frank"
S.plot	If TRUE, the survival curve is displayed
N	The number of permutations
mark.time	If TRUE, then curves are marked at each censoring time

Details

Two-sample comparison based on estimated survival functions via copula-graphic estimators under dependent censoring. The D statistic (the mean vertical difference betewen two estimated survival functions) is used for testing the null hypothesis of no difference in survival. See Yeh et al.(2023) for details.

Value

test	Testing the difference of two survival functions
Good	Good prognostic group defined by PI<=c
Poor	Poor prognostic group defined by PI>c

Author(s)

Takeshi Emura, Pauline Baur

References

Emura T, Chen YH (2018). Analysis of Survival Data with Dependent Censoring, Copula-Based Approaches, JSS Research Series in Statistics, Springer, Singapore.

Rivest LP, Wells MT (2001). A Martingale Approach to the Copula-graphic Estimator for the Survival Function under Dependent Censoring, J Multivar Anal; 79: 138-55.

Yeh CT, Liao GY, Emura T (2023). Sensitivity analysis for survival prognostic prediction with gene selection: a copula method for dependent censoring, Biomedicines 11(3):797.

Examples

t.vec=c(1,3,5,4,7,8,10,13)
d.vec=c(1,0,0,1,1,0,1,0)
PI=c(8,7,6,5,4,3,2,1)

CG.test(t.vec,d.vec,PI,copula=CG.Clayton,alpha=18,N=100)
CG.test(t.vec,d.vec,PI,copula=CG.Gumbel,alpha=2,N=100)

compound.Cox documentation built on July 26, 2023, 5:39 p.m.