[R] Kaplan Meier analysis: 95% CI wider in R than in SAS

[Frank Harrell]

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)
>  
> 
> ______________________________________________
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> 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
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