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Re: st: comparison of diagnostic procedures


From   Roger Harbord <roger.harbord@bristol.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: comparison of diagnostic procedures
Date   Fri, 09 Sep 2005 09:40:54 +0100

Try -serrbar- or -twoway rcap-. However you'd need to first save the estimates and CIs as variables. Roger Newson's -parmest- package could be one way to do that, after which you could use his -eclplot- package (both available on SSC) as an alternative to -serrbar- or -twoway rcap-.

Roger H.

--On 08 September 2005 16:15 +0300 htzavara@med.uoa.gr wrote:


Αρχικό μήνυμα από  Roger Harbord <roger.harbord@bristol.ac.uk>:

thank you a lot for your help. This is the solution and i have already
find it  in a related article. I would like to ask you if you know how i
will produce  an error graph (graph of sensitivities and their confidence
interval) for  every diagnostic test? Does stata 8 support a graph like
this?

thank you a lot in advance!!!!!!


As Pepe mentions on p43, you can test the null hypothesis of equal
sensitivity or  of equal specificity of two binary tests done on the
same  people using McNemar's test (-symmetry- or -mcc- commands in
Stata). I  think something like:

. symmetry test1 test2 if disease==1   /* for sensitivity */
. symmetry test1 test2 if disease==0   /* for specificity */


However with 12 tests there are a lot of comparisons (66 for each of
sens &  spec) so some allowance for multiple testing does seem a good
idea.

A Bayesian approach seems quite attractive for this sort of problem as
you  can then meaningfully ask "what is the probability that test X has
the  highest sensitivity?", which you can't in a frequentist framework.
You'd  need to switch to something like WinBUGS to get an answer to that
though.

If one test has higher sensitivity than another but lower specificity or
vice-versa then which is better also depends on the disbenefits of false
positives compared to false negatives of course.

Roger.

--
Roger Harbord                             roger.harbord@bristol.ac.uk
MRC Health Services Research Collaboration & Dept. of Social Medicine
University of Bristol        http://www.epi.bris.ac.uk/staff/rharbord

--On 07 September 2005 15:07 -0400 "Michael P. Mueller"
<michael.mueller@utoronto.ca> wrote:

> You might want to take a look at this book: Pepe, M.S. (2003).
> Statistical Evaluation of Medical Tests for Classification and
> Prediction. Dr. Pepe has Stata programs on her webpage you can
> download. Hope this helps,
> Michael
>
> htzavara@med.uoa.gr wrote:
>
>> Αρχικό μήνυμα από  Svend Juul <SJ@SOCI.AU.DK>:
>>
>>
>>
>>> 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.

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