[R] net classification improvement?

[Kevin E. Thorpe]

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