HOW to use the survivalROC to get optimal cut-off values?

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".

Snipped comments about small nubers of discrete values as that was a  
misunderstanding on my part.

--------------------------------------------------------------------------------

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)

I really do not understand what you are asking when you ask about "5  
values". (I also see that another student GY QIAN in SHANGHAI,CHINA  
asked almost the same odd question on rhelp about 6 weeks ago making  
me wonder if there is some online class you are both taking for which  
this is homework?)

  There are 312 finite values for each of cut.values, FP, and TP. Is  
someone, your course instructor or collaborator, asking for a  
comparison at 5 selected cutpoints? I generally use Hmisc::describe  
for quick looks:
 > describe(ROC.1$cut.values[-1])
ROC.1$cut.values[-1]
       n missing  unique    Mean     .05     .10     .25     .50     . 
75     .90     .95
     312       0     312   6.538   5.320   5.459   5.842   6.312    
6.949   7.966   8.675

lowest :  4.581  4.900  4.926  4.932  4.946, highest:  9.510  9.568  
10.185 10.479 10.629


You could programmatically ask what values of the index (1:313)   
minimizes the sum of FN and (1-TP) = FP. And then you can use to index  
the TP and "FP" values. I guess it is common practice to call 1- 
specificity the "false positive rate" but I for one find that very  
confusing. even misleading, since it's not clear from the term what  
the denominator for such a "rate" really should be.  (It's also not  
really a rate since no time is involved in the calculation.) At any  
rate, as it were:

 > with(ROC.1, which.min(1-TP+ FP))
[1] 259

 > with(ROC.1, points(FP[259], TP[259], cex=3, col="red" ))

David Winsemius, MD
West Hartford, CT