In small to moderate sample sizes, the Harrell-Davis quantile estimator is more accurate than the ordinary sample quantile, and there is a good standard error estimator for it using U-statistics. See the hdquantile function in the Hmisc package. Frank swatch110362 wrote:> > hi~ > I need to estimate the standard error of the quantile in survival > analysis. > Can anyone help me,please? >----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/the-standard-error-of-the-quantile-tp4086479p4086494.html Sent from the R help mailing list archive at Nabble.com.
Thanks for your help. But I need to estimator the standard error of the quantile in "survival analysis", because my data is censored. For example~ T<-c(84,240,261,332,348,437,521,565) S<-c(0,1,1,0,1,0,1,0) ##0 for censoed; 1 for event G<-rep(1,8) ori_s.surv<-survfit(Surv(T,S)~G) -- View this message in context: http://r.789695.n4.nabble.com/the-standard-error-of-the-quantile-tp4086479p4086512.html Sent from the R help mailing list archive at Nabble.com.
Then see the latter part of my note. I think also that, at least for the median, the survival package will compute it more quickly for inclusion in a bootstrap loop. Note that you forgot to state require(survival) or library(survival) below. Frank swatch110362 wrote:> > Thanks for your help. > But I need to estimator the standard error of the quantile in "survival > analysis", because my data is censored. > For example~ > > > T<-c(84,240,261,332,348,437,521,565) > S<-c(0,1,1,0,1,0,1,0) ##0 for censoed; 1 for event > G<-rep(1,8) > > ori_s.surv<-survfit(Surv(T,S)~G) >----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/the-standard-error-of-the-quantile-tp4086479p4087199.html Sent from the R help mailing list archive at Nabble.com.