DAVID ARTETA GARCIA wrote:
R-users,
I am bootstrapping the C Index of a model created using lrm{Design} and
boot{boot}, and I get that the upperbound of the confidence interval is
greater than 1. Here is my code:
library(HSAUR)
data(plasma)
##fit model
fit.design <- lrm (ESR ~ fibrinogen + globulin,data=plasma)
fit.design$stats[6]
C
0.8044872
##bootstrap C Index
cindex <- function(formula,data,indices){
+ d=data[indices,]
+ fit<-lrm(formula,data = d)
+ return(fit$stats[[6]])
+ }
results <- boot(data=w,statistic=cindex,R=500,formula = ESR ~
fibrinogen + globulin)
results
ORDINARY NONPARAMETRIC BOOTSTRAP
Call:
boot(data = plasma, statistic = cindex, R = 500, formula = ESR ~
fibrinogen +
globulin)
Bootstrap Statistics :
original bias std. error
t1* 0.8044872 0.008834767 0.1574710
boot.ci(results,type="basic")
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 500 bootstrap replicates
CALL :
boot.ci(boot.out = results, type = "basic")
Intervals :
Level Basic
95% ( 0.6090, 1.1443 )
Calculations and Intervals on Original Scale
I see that the std.error is rather large and this might be the problem,
but how can I explain this for publication purposes? Is such an interval
acceptable?
Any help would be greatly appreciated
David
A few observations.
1. With minimal overfitting, rcorr.cens(predict(fit), Y) gives a good
standard error for Dxy = 2*(C-.5) and bootstrapping isn't very necessary
2. If you bootstrap use the nonparametric bootstrap percentile method or
other methods that constrain the confidence interval to be in [0,1].
3. I don't know why the model would be linear on the two predictors you
are using.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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