R help - Dec 2010 - HOW to use the survivalROC to get optimal cut-off values?

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.
--------------------------------------------------------------------------------

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
Facolt? di Medicina e Chirurgia
Universit? di Napoli Federico II
081 - 7462233

On Dec 5, 2010, at 11:14 AM, petretta at 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
> Facolt? di Medicina e Chirurgia
> Universit? di Napoli Federico II
> 081 - 7462233
>
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West Hartford, CT