[R] Difference between R and SAS in Corcordance index in ordinal logistic regression

[Frank Harrell]

lrm does some binning to make the calculations faster.  The exact calculation
is obtained by running

f <- lrm(...)
rcorr.cens(predict(f), DA), which results in:

       C Index            Dxy           S.D.              n        missing 
    0.96814404     0.93628809     0.03808336    32.00000000     0.00000000 
    uncensored Relevant Pairs     Concordant      Uncertain 
   32.00000000   722.00000000   699.00000000     0.00000000 

I.e., C=.968 instead of .963.  But this is even farther away than the value
from SAS you reported.

If you don't believe the rcorr.cens result, create a tiny example and do the
calculations by hand.
Frank


blackscorpio81 wrote
> Dear R users,
> 
> Please allow to me ask for your help.
>  I am currently using Frank Harrell Jr package "rms" to model ordinal
> logistic regression with proportional odds. In order to assess model
> predictive ability, C concordance index is displayed and equals to 0.963.
> 
> This is the code I used with the data attached 
> data.csv <http://r.789695.n4.nabble.com/file/n4656409/data.csv>  
>  :
> 
>>require(rms)
>>a<-read.csv2("/data.csv",row.names = 1,na.strings = c(""," "),dec=".")
>>lrm(DA~SJ+TJ,data=a)
> 
> Logistic Regression Model
> 
> lrm(formula = DA~SJ+TJ, data = a)
> 
> Frequencies of Responses
> 
>  1  2  3  4 
>  6 13  9  4 
> 
>                                               Model Likelihood         
> Discrimination                  Rank Discrim.    
>                                              Ratio Test                       
> Indexes                               Indexes       
> Obs            32                      LR chi2      53.14             R2      
> 0.875                      C       0.963    
> max |deriv| 6e-06             d.f.             2                    g             
> 8.690                Dxy     0.925    
>                                              Pr(> chi2) <0.0001         gr   
> 5942.469                    gamma   0.960    
>                                                                                      
> gp       0.486                      tau-a   0.673    
>                                                                                      
> Brier    0.022                     
> 
>                         Coef              S.E.        Wald  Z     Pr(>|Z|)
> y>=2             -0.6161     0.6715        -0.92           0.3589  
> y>=3             -6.5949     2.3750        -2.78          0.0055  
> y>=4        -16.2358        5.3737         -3.02         0.0025  
> SJ                 1.4341      0.5180          2.77         0.0056  
> TJ                  0.5312      0.2483         2.14          0.0324
> 
> I wanted to compare the results with SAS. I found the same slopes and
> intercept with opposite signs, which is normal since R models the
> probabilities P(Y>=k|X) whereas SAS models the probabilities P(Y<=k|X) 
> (see pdf attached, page 2 , table "Association des probabilités prédites
> et des réponses observées").
> SAS_Report_-_Logistic_Regression.pdf
> <http://r.789695.n4.nabble.com/file/n4656409/SAS_Report_-_Logistic_Regression.pdf>  
> 
> I chose the order for levels.
> 
> I controlled that the corresponding probabilities P(Y=k|X)  are the same
> with both softwares. But I can't understand why in SAS the C index drops
> from 0.963 down to 0.332.
> 
> I read a lot of things about this and it seems to me that both softwares
> use slightly different technique to compute the C index ; it is
> nevertheless surprising to me to observe such a shift in the results.
> 
> Does anyone have a clue on this ?
> Thank you very much for you help
> Blackscorpio





-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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