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.
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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
