Make sure you use the log S(t) basis on both systems (and avoid log-log S(t) basis as this results in instability in the front part of the survival curve). Frank
Paul Miller wrote > > Hi Enrico, > > Not sure how SAS builds the CI but I can look into it. The SAS > documentation does have a section on computational formulas at: > > http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_lifetest_a0000000259.htm > > Although I can't provide my dataset, I can provide the data and code > below. This is the R-equivalent of an analysis from "Common Statistical > Methods for Clinical Research with SAS Examples." > > R produces the follwoing output: > >> print(surv.by.vac) > Call: survfit(formula = Surv(WKS, CENS == 0) ~ VAC, data = hsv) > > records n.max n.start events median 0.95LCL 0.95UCL > VAC=GD2 25 25 25 14 35 15 NA > VAC=PBO 23 23 23 17 15 12 35 > > SAS has the same 95% CI for VAC=GD2 but has a 95% CI of [10, 27] for > VAC=PBO. This is just like in the analysis I'm doing currently. > > Thanks, > > Paul > > > ####################################### > #### Chapter 21: The Log-Rank Test #### > ####################################### > > ##################################################### > #### Example 21.1: HSV2 Vaccine with gD2 Vaccine #### > ##################################################### > > connection <- textConnection(" > GD2 1 8 12 GD2 3 -12 10 GD2 6 -52 7 > GD2 7 28 10 GD2 8 44 6 GD2 10 14 8 > GD2 12 3 8 GD2 14 -52 9 GD2 15 35 11 > GD2 18 6 13 GD2 20 12 7 GD2 23 -7 13 > GD2 24 -52 9 GD2 26 -52 12 GD2 28 36 13 > GD2 31 -52 8 GD2 33 9 10 GD2 34 -11 16 > GD2 36 -52 6 GD2 39 15 14 GD2 40 13 13 > GD2 42 21 13 GD2 44 -24 16 GD2 46 -52 13 > GD2 48 28 9 PBO 2 15 9 PBO 4 -44 10 > PBO 5 -2 12 PBO 9 8 7 PBO 11 12 7 > PBO 13 -52 7 PBO 16 21 7 PBO 17 19 11 > PBO 19 6 16 PBO 21 10 16 PBO 22 -15 6 > PBO 25 4 15 PBO 27 -9 9 PBO 29 27 10 > PBO 30 1 17 PBO 32 12 8 PBO 35 20 8 > PBO 37 -32 8 PBO 38 15 8 PBO 41 5 14 > PBO 43 35 13 PBO 45 28 9 PBO 47 6 15 > ") > > hsv <- data.frame(scan(connection, list(VAC="", PAT=0, WKS=0, X=0))) > hsv <- transform(hsv, > CENS = ifelse(WKS < 1, 1, 0), > WKS = abs(WKS), > TRT = ifelse(VAC=="GD2", 1, 0)) > > library("survival") > surv.by.vac <- survfit(Surv(WKS,CENS==0)~VAC, data=hsv) > > plot(surv.by.vac, > main = "The Log-Rank Test \n Example 21.1: HSV-Episodes with gD2 > Vaccine", > ylab = "Survival Distribution Function", > xlab = "Survival Time in Weeks", > lty = c(1,2)) > > legend(0.75,0.19, > legend = c("gD2","PBO"), > lty = c(1,2), title = "Treatment") > > summary(surv.by.vac) > print(surv.by.vac) > > > ______________________________________________ > R-help@ mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Kaplan-Meier-analysis-95-CI-wider-in-R-than-in-SAS-tp4554559p4555447.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.