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
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