On Dec 5, 2010, at 11:14 AM, petre...@unina.it wrote:
I have the same problem of a prevous request
HOW to use the survivalROC (or another library in R) to get optimal
cut-off values?
I want to use the time-dependent survivalROC package.according to
the,reference material,it only gives a set of ordered cut-off
values .eg.
Optimality specification requires some sort of valuation of incorrect
decisions. If you are willing to defend a choice that a false positive
has exactly the same loss as a false negative, which is generally not
the case in medical decision-making, then the point on the ROC curve
which is closest to the upper left-hand corner is "optimal".
Having only 5 values is getting pretty close to violating the
presumption of ROC analysis that the result be at least pseudo-
continuous. I have see quite a few "ROC curves in this situation that
do not have a clear winner ( now assuming equal cost for FP and FN
which I already said was usually a faulty assumption) because the
closest point on the curve was in the middle of the line segment
between two of the points. I'm not sure that the typical practice of
plotting ROC curves with slanting line segments is valid. There is no
information between those discrete points. You should probably be
using a table rather than a curve in this situation.
--------------------------------------------------------------------------------
data(mayo)
str(mayo)
attach(mayo)
ROC.
1
=
survivalROC
(Stime
=time,status=censor,marker=mayoscore4,predict.time=365,lambda=0.05)
str(ROC.1)
plot(ROC.1$FP, ROC.1$TP, type="l", xlim=c(0,1), ylim=c(0,1),
xlab=paste( "FP", "\n", "AUC = ",round(ROC.1$AUC,3)),
ylab="TP",main="Mayoscore 4, Method = NNE \n Year = 1") abline(0,1)
List of 6
$ cut.values : num [1:313] -Inf 4.58 4.9 4.93 4.93 ... *only 5 values
* $ TP : num [1:313] 1 0.999 0.999 0.999 0.998 ...
$ FP : num [1:313] 1 0.997 0.993 0.99 0.987 ...
$ predict.time: num 365
$ Survival : num 0.93
$ AUC : num 0.888
--------------------------------------------------------------------------------
so i dont know
how to use the survivalROC to get optimal cut-off values?(only 5
values)
Thank you very much!
Mario Petretta
Dipartimento di Medicina Clinica Scienze Cardiovascolari e
Immunologiche
Facoltà di Medicina e Chirurgia
Università di Napoli Federico II
081 - 7462233
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