net classification improvement?
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.thorpe at 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.thorpe at utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016