Perform factorial survival analysis under dependent censoring under an assumed copula (Emura et al. 2023-).
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)Copula parameter
Estimates of treatment effects
Variance estimates
F-statistic
Critical value via the simulation method
Critical value via the analytical method
P-value of the F-test
Vector of survival times (time to either death or censoring)
Vector of censoring indicators, 1=death, 0=censoring
Vector of group indicators, 1, 2, ..., d
Copula function: "CG.Clayton","CG.Gumbel" or "CG.Frank"
Copula parameter
The number of Monte Carlo simulations to find the critical value of the F-test
Follow-up end (default is max(t.vec))
Contrast matrix
If TRUE, the survival curve is displayed
If TRUE, then curves are marked at each censoring time
Takeshi Emura
Estimates of treatment effects and the test results are shown.
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.