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st: RE: Comparing AuROCs for same predictor but different outcomes


From   "Dwamena, Ben" <bdwamena@med.umich.edu>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Comparing AuROCs for same predictor but different outcomes
Date   Fri, 6 Apr 2012 14:29:30 +0000

You can use roccomp or If you have Stata 12, rocreg.

Ben A. Dwamena, MD

Division of Nuclear Medicine and Molecular Imaging
Department of Radiology
B1 G-505 UH
1500 E. Medical Center Drive
Ann Arbor, MI  48109-5030
Ph: 734-936-5387 
Fx: 734-936-8182 
http://sitemaker.umich.edu/metadiagnosis/midas_home

Nuclear Medicine Service (115)
VA Ann Arbor  Health Care System
2215 Fuller Road
Ann Arbor, MI 48105
Ph: 734-845-3775 

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Anna DeBenedetti Rubinsky
Sent: Thursday, April 05, 2012 5:56 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: Comparing AuROCs for same predictor but different outcomes

Hello,

We have data from a stratified random sample, with final weights to account for the sampling design and nonresponse.
We would like to compare AuROCs for a single screening instrument based on different variants of the outcome (because diagnostic criteria for the disease of interest are changing).

We have been able to estimate separate AuROCs and CIs using Roger Newson's somersd command, factoring in the sampling design (pweight=final weight; cluster=primary sampling unit) per the suggestion on http://www.stata.com/statalist/archive/2009-01/msg00689.html.

However, I am not sure how to compare these AuROCs (from the same
sample) and estimate the CI and p-value for the difference.

The listserve has addressed how to compare AuROCs for two predictors of the same outcome (http://www.stata.com/statalist/archive/2012-01/msg00477.html),
but I have not been able to figure how to compare AuROCs of the same predictor for two variations of the outcome, especially given that the data is from a survey with a complex sampling design.

Advice would be much appreciated.
Thank you,
Anna
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