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

[David Winsemius]

On Feb 21, 2012, at 3:21 AM, David Winsemius wrote:

>
> On Feb 21, 2012, at 12:08 AM, alexiamelissa wrote:
>
>> I have a follow up question to Dr Winsemius' post.  You can use the  
>> AIC
>> criterion against all possible cut off values C to see which  
>> minimizes the
>> AIC and then that is the ideal cut off in trying to dichotomize a  
>> continuous
>> variable.  What I am wondering here is, does the survivalROC  
>> package, or any
>> other package in R or function in SAS compute this?  I have been  
>> reading and
>> this does not seem to be addressed anywhere so please point me in  
>> the right
>> direction.
>
Errors (mostly?) corrected:

The usual attempts to set a cut-point make the very restrictive and  
simplistic assumption that the costs of a decision resulting in a  
false positive are the same as the costs of a false positive. This is  
almost never the case. Furthermore, these studies are often done with  
case and control populations that are not representative of the  
populations for which the test will be applied in the future. I think  
handing off the task to an automatic procedure dressed-up to construct  
an "ideal" or "scientific" answer is misguided. They are an effort to  
avoid thinking carefully about the costs of the alternative outcomes,  
and fail to account for the reality that there are multiple parties  
being affected with no meaningful input regarding their respective  
utilities.

  Apologies.

>
> I'm not saying that quantitative analysis of these issues is not  
> useful, just that it is unlikely to be done well by one function in  
> a package in R or SAS..
>
> -- 
>

David Winsemius, MD
West Hartford, CT