On 01/17/2012 07:16 AM, Essers, Jonah wrote:
Thanks for the reply. I think more the issue is whether it can be applied
to cross-sectional data. This I'm not sure. This method is heavily cited
in the New England Journal of Medicine, but thus far I've only seen it
used with longitudinal data.
As I recall, the Pencina et al paper does not suggest it cannot be used
outside of longitudinal data. In fact, I don't remember them using
longitudinal data at all. So, unless I'm misunderstanding your
question, I think the function in Hmisc (whose name I always forget)
should be fine.
On 1/16/12 10:23 PM, "Kevin E. Thorpe"<kevin.tho...@utoronto.ca> wrote:
On 01/16/2012 08:10 PM, Essers, Jonah wrote:
Greetings,
I have generated several ROC curves and would like to compare the AUCs.
The data are cross sectional and the outcomes are binary. I am testing
which of several models provide the best discrimination. Would it be
most
appropriate to report AUC with 95% CI's?
I have been looking in to the "net reclassification improvement" (see
below for reference) but thus far I can only find a version in Hmisc
package which requires survival data. Any idea what the best approach is
for cross-sectional data?
I believe that the function in Hmisc that does this will also work on
binary data.
Thanks
Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the
added predictive ability of a new marker: from area under the ROC curve
to
reclassification and beyond. Stat Med 2008;27:157-172
--
Kevin E. Thorpe
Biostatistician/Trialist, Applied Health Research Centre (AHRC)
Li Ka Shing Knowledge Institute of St. Michael's
Assistant Professor, Dalla Lana School of Public Health
University of Toronto
email: kevin.tho...@utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016
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