CG.test {compound.Cox} R Documentation

## 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)


[Package compound.Cox version 3.30 Index]