Re: st: Question on ROC analysis

[Steven Samuels <sjsamuels@gmail.com>]

Megan,

Another advantage of using -somersd-: It can produce asymmetric confidence intervals for the AUC, which will often be more accurate for high or low values of the AUC. You do this by computing the intervals for Fisher's Z transform of Somers' D statistic, then transforming them by hand to intervals for the AUC ("Harrell's c").

Ref: Roger Newson Confidence intervals for rank statistics: Somers’ D and extensions.  The Stata Journal (2006) 6, Number 3, pp. 309–334 available at: http://www.stata-journal.com/article.html?article=snp15_6

**************************CODE BEGINS*******
sysuse auto, clear
gen y = 1/mpg
gen ylow = y<.05
gen yhigh = 1-ylow
gen x = weight
gen nx = -x

somersd ylow nx, tdist tr(z)  
matrix b = e(b)
local z= b[1,1]
matrix v=e(V)
local v =v[1,1]

local bound = sqrt(`v')*invttail(e(df_r),0.5*(1-c(level)/100))
local ll   = `z'-`bound'
local ul   = `z' + `bound'
local auc  = (exp(2*`z')-1)/(exp(2*`z')+1)
local auc  = (`auc'+1)/2
local llim = (exp(2*`ll')-1)/(exp(2*`ll')+1)
local llim = (`llim'+1)/2
local ulim = (exp(2*`ul')-1)/(exp(2*`ul')+1)
local ulim = (`ulim'+1)/2
//asymmetric interval
di "Asymmetric: auc ="%9.7g `auc' " llim ="%9.7g `llim'  " ulim ="%9.7g `ulim'  
// compare to symmetric interval:
somersd ylow nx, tdist tr(c)  
**************************CODE ENDS*******

Steve
sjsamuels@gmail.com





On May 23, 2011, at 5:29 PM, Megan Deitchler wrote:

Thanks - this helped.  I can now graph what I want but am still having
trouble calculating the AUC.

I am trying to use Roger Newson's somersd package for this, using the
c transformation option.  I receive the following error:

     tidotforsomersd():  3499  tidottree() not found
               <istmt>:     -  function returned error

Any suggestions?

On Thu, May 19, 2011 at 3:19 PM, Steven Samuels <sjsamuels@gmail.com> wrote:
> 
> Roger -senspec- from SSC should do what you want.
> 
> Steve
> sjsamuels@gmail.com
> 
> On May 19, 2011, at 2:46 PM, Megan Deitchler wrote:
> 
> I am interested in carrying out a simple two variable ROC analysis.
> 
> I want to assess how well low values of my x variable predict a low value of
> my y variable (e.g. y variable with cutoff less than 200).
> 
> However, if I understand correctly, the conventional ROC analysis in Stata
> creates the ROC by using incrementally increasing values of x to predict y.
> 
> How do I adapt the analysis so that the AUC result I obtain will
> be consistent with the relationship I am interested in quantifying?
> 
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