SAS and R survival package have different confidence interval estimates. Why? Hello I am learning about Survival Analysis and I noticed that SAS and R survival package produce different confidence interval estimates for the median Survival times and I was curious why?
Generating my simulated data in R
set.seed(123)
library(survival)
size <-  100

deathtime <- rexp(size, rate = 1)
censor <- abs(rbinom(size, 1, .1) -1)



df <- data.frame(deathtime, censor)
#write.csv(df, "TestData.csv") #For uses in SAS

Using the Survival package
surv.obj <- Surv(df$deathtime, df$censor)

survfit(surv.obj ~ 1)


Using SAS
proc import datafile="P:\SAS\Interval\TestData.csv" out = test;
run;

PROC LIFETEST PLOTS=(S) METHOD=PL;                                                    
     TIME   deathtime *censor(0) ;                                                                                                                 
 RUN;


Question
The point estimate is the same though the confidence intervals differs why?

There are several ways to compute confidence intervals (CI) for survival curves. The SAS output suggests that it uses a log-log method based on $\log(-\log(\text{survival}))$.
Check the R documentation for survfit.formula to see its options for CI estimation. There is a default method for that function in R that isn't log-log. See what happens when you specify "log-log" as the method in R. Then, as suggested in a comment on your question, see what happens when you use SAS with different CI options.
