surv.factorial {compound.Cox}

Factorial survival analysis under dependent censoring

Description

Perform factorial survival analysis under dependent censoring under an assumed copula (Emura et al. 2023-).

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 (2023-), Factorial survival analysis for treatment effects under dependent censoring, in preparation.

Emura T, Matsui S, Chen HY (2019). compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival, Computer Methods and Programs in Biomedicine 168: 21-37.

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.

Examples

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