[R] Weighted Kaplan-Meier estimates with R (with confidenceintervals)?

[Blaser Nello]

The two confidence intervals should be different. In the first model you have 3 failures and the second one you have 300. More failures results in narrower confidence intervals. 


-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of rm
Sent: Montag, 25. März 2013 10:47
To: r-help at r-project.org
Subject: [R] Weighted Kaplan-Meier estimates with R (with confidenceintervals)?

As part of a research paper, I would like to draw both weighted and unweighted Kaplan-Meier estimates, the weight being the ’importance’ of the each project to the mass of projects whose survival I’m trying to estimate.

I know that the function survfit in the package survival accepts weights and produces confidence intervals. However, I suspect that the confidence intervals may not be correct. The reason why I suspect this is that depending on how I define the weights, I get very different confidence intervals, e.g.

require(survival)
s <- Surv(c(50,100),c(1,1))
sf <- survfit(s~1,weights=c(1,2))
plot(sf) 

vs.

require(survival)
s <- Surv(c(50,100),c(1,1))
sf <- survfit(s~1,weights=c(100,200))
plot(sf)

Any suggestions would be more than welcome!




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