
R Under development (unstable) (2020-03-27 r78086) -- "Unsuffered Consequences"
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> library(survival)
> #
> # Check that the survival curves from a Cox model with beta=0 
> #  match ordinary survival
> #
> #  Aalen
> surv1 <- survfit(Surv(time,status) ~ sex, data=lung, stype=2)
> fit1 <- coxph(Surv(time, status) ~ age + strata(sex), data=lung, iter=0,
+               ties='breslow')
> fit1$var <- 0*fit1$var   #sneaky, causes the extra term in the Cox variance
>                          # calculation to be zero
> surv2 <- survfit(fit1, stype=2)
> surv3 <- survfit(fit1)
> 
> arglist <- c('n', 'time', 'n.risk','n.event', 'n.censor', 'surv', 'strata',
+              'std.err', 'upper', 'lower')
> all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
[1] TRUE
> all.equal(unclass(surv1)[arglist], unclass(surv3)[arglist])
[1] TRUE
> 
> 
> # Efron method
> surv1 <- survfit(Surv(time,status) ~ sex, data=lung, stype=2, ctype=2)
> surv2 <- survfit(fit1, ctype=2)
> all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
[1] TRUE
> 
> # Kaplan-Meier
> surv1 <- survfit(Surv(time,status) ~ sex, data=lung)
> surv2 <- survfit(fit1, stype=1)
> all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
[1] TRUE
> 
> 
> # Now add some random weights
> rwt <- runif(nrow(lung), .5, 3)
> surv1 <- survfit(Surv(time,status) ~ sex, data=lung, stype=2, weight=rwt,
+                  robust=FALSE)
> fit1 <- coxph(Surv(time, status) ~ age + strata(sex), data=lung, iter=0,
+               ties='breslow', weight=rwt, robust=FALSE)
> fit1$var <- 0*fit1$var   #sneaky
> surv2 <- survfit(fit1, stype=2, ctype=1)
> surv3 <- survfit(fit1)
> 
> all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
[1] TRUE
> all.equal(unclass(surv1)[arglist], unclass(surv3)[arglist])
[1] TRUE
> 
> 
> # Efron method
> surv1 <- survfit(Surv(time,status) ~ sex, data=lung, stype=2, ctype=2,
+                  weight=rwt, robust=FALSE)
> surv2 <- survfit(fit1, ctype=2, robust=FALSE)
> all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
[1] TRUE
> 
> # Kaplan-Meier
> surv1 <- survfit(Surv(time,status) ~ sex, data=lung, weight=rwt, robust=FALSE)
> surv2 <- survfit(fit1, stype=1, robust=FALSE)
> all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
[1] TRUE
> 
> 
> proc.time()
   user  system elapsed 
  0.940   0.037   0.969 
