������ ������ ��� Svend Juul <[email protected]>:
htzvara (?) wrote:
i have one variable which represents if the patient has the disease
(coding: 0-
1)--and this is standard.
Additionally i have 12 more variables which represents the outcome of 12
different diagnostic procedures (coding: 0-1 for all of them).I want to
find which is the best diagnostic procedure. I calculate the
sensitivity and specificity and their confidence intervals for each of
them. If the confidence
interval for the sensitivity of one diagnostic procedure do not overlap
the confidence interval for the Se of another diagnostic procedure then
the difference is significant.
Is there any test to perform and give p_value? Is there a need to make a
correction for multiple comparisons.?
----
It is not quite clear to me what you want. If it is to find the single
test that has the "best" predictive value, try Paul Seed's -diagt-
(findit diagt). However, you must look at both sensitivity and
specificity to get a meaningful assessment.
I am not sure why you want to test whether the sensitivity of two tests
are significantly different. And the confidence interval comparison you
describe is quite insensititive.
Would this show what you need:
Make a logistic regression followed by -lroc- (ROC analysis):
. logistic disease test1-test12
. lroc
You might then try to remove tests to see whether removal makes a
difference to the AUC (area under curve).
Hope this helps
Svend
Thank you very much for your help.
I know about diagt and i used it to obtain the sensitivity - specificity.
Roc analysis cannot help as the variables which represents the
diagnostic tests are not continuous but dichotomous (0-1).Even if i can
see, which test has the best se-sp i want to perform a test to prove it.
thank you again.
