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

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)

enter image description here

Using SAS

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

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

enter image description here


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

  • 2
    $\begingroup$ Have you tried other transform? According to the LIFETEST procedure you can choose the transform using the CONFTYPE option documentation.sas.com/doc/en/statug/15.2/…. More details regarding computation of the confidence interval are given on page 33 here support.sas.com/documentation/onlinedoc/stat/141/lifetest.pdf $\endgroup$
    – periwinkle
    Commented Oct 13, 2021 at 18:11
  • 1
    $\begingroup$ Yes @periwinkle as mentioned by EdM R has the conf.type argument to change the transform whose default is Log and SAS has the CONFTYPE to change the transform which default is log log. We figured it out $\endgroup$
    – Vefeagins
    Commented Oct 13, 2021 at 18:34

1 Answer 1


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.

  • 4
    $\begingroup$ Yes survfit(surv.obj ~ 1, conf.type = "log-log") Produced the same results as SAS $\endgroup$
    – Vefeagins
    Commented Oct 13, 2021 at 18:25
  • 4
    $\begingroup$ And adding a to SAS CONFTYPE= LOG leads to the same result in R. $\endgroup$
    – Vefeagins
    Commented Oct 13, 2021 at 18:32

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