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! -- View this message in context: http://r.789695.n4.nabble.com/Weighted-Kaplan-Meier-estimates-with-R-with-confidence-intervals-tp4662360.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.
