Concordance vs. Sensitivity

[Ujjawal]

I am confused between these two terms - Sensitivity and Concordance. What i understand about these two terms -

Concordance - the number of pairs where actual 1s have higher predicted probability of becoming 1s than 0s divided by total number of pairs

Sensitivity (True Positive) - percentage of 1s correctly predicted as 1. By their definitions, both seems to be the same.

I have read somewhere Concordance = (TRUE POSITIVE + TRUE NEGATIVE) / SAMPLE SIZE. I have tried to calculate concordance using this formula. But it seems to be incorrect.

First, i have run logistic regression using PROC LOGISTIC with CTABLE PPROB=0.5 options. Then, i have calculated true positive pairs from (Sensitivity percentage* number of 1s) and true negative pairs from (Specificity percentage * number of 0s). I added them up and divided by total responses. It is not matching up with concordance score.

[Quentin]

Hi,

I think you have two different definitions of concordance.

If I heard "concordance" in a diagnostic setting or predictive setting, I would first think of your second definition (TRUE POSTIVE + TRUE NEGATIVE) / SAMPLE SIZE.

Sensitivity is (True Positive)/(True Positive + False Negative).

There are lost of good pages on sensitivity/specificity/PPV/NPV etc.  Wikipedia has a nice 2x2 contingency table which describes different statistics.http://en.wikipedia.org/wiki/Sensitivity_and_specificity

But it looks like proc logistic is giving you something else for CONCORDANCE.  It is not %agreement using sample size as the denominator.  This site has a nice explanation.

http://www.ats.ucla.edu/stat/sas/output/sas_logit_output.htm

Note the denominator used by this calculation is not the sample size, it's the number of pairs.

Below is sample data.

Concordance of the contingency table is (TP+TN)/SIZE = (40+50)/100= 90%.

The concordance calculated by LOGISTIC is 80.8%.  The denominator for that calculation is 2475, i.e. with 45 true negatives and 55 true positive, you can have 2475 pairs.

data a;
   input x y weight;
   cards;
0 0 40
0 1 5
1 1 50
1 0 5
;
run;

proc logistic data=a descending;
  class x;
  model y=x;
  freq weight;
run;

proc freq data=a ;
  tables x*y /measures;
  weight weight;
run;

HTH,

--Q.

[stat_sas]

Hi,

Why are you using PPROB=0.5 in PROC LOGISTIC options? Why not 0.4,0.3 or some other value?