Dear Sir or Madam：
I am a doctor of urology,and I am engaged in developing a nomogram of bladder
cancer. May I ask for your help on below issue?
I set up a dataset which include 317 cases. I got the Binary Logistic
Regression model by SPSS.And then I try to reconstruct the model
（lrm(RECU~Complication+T.Num+T.Grade+Year+TS)） by R-Project,and try to internal
validate the model through using the function “validate( )”,and get the ROC
through the function “plot.roc( )”.The outcomes like this: At last I want to
get the Logistic model ,and get the prediction accuracy .Now the “Area under
the curve”(0.6931) is not too bad,but the “Dxy”(I think it as the prediction
accuracy probability) is too low.And I don’t know which reason lead to the
outcomes.Maybe I have a mistake understanding on the function “lrm( )”,and
apply it wrong.
Could you please give me some idea on how to resulve this problem? Thanks in
advance for your kind support.
warmly regards,
Ding
---------------------------------------outcomes----------------------------------------------------------------------------
Logistic Regression Model
lrm(formula = RECU ~ Complications + T.Num + T.Grade + Year + TS, x = TRUE, y =
TRUE)
Model Likelihood Discrimination
Rank Discrim.
Ratio Test Indexes
Indexes
Obs 317 LR chi2 37.78 R2 0.154
C 0.693
0 201 d.f. 5 g 0.876
Dxy 0.386
1 116 Pr(> chi2) <0.0001 gr 2.400
gamma 0.408
max |deriv| 2e-09 gp 0.183
tau-a 0.180
Brier
0.207
Coef S.E.
Wald Z Pr(>|Z|)
Intercept -2.3566 0.3819 -6.17
<0.0001
Complications 1.6807 0.6005 2.80
0.0051
T.Num 0.6481 0.2503 2.59
0.0096
T.Grade 0.4276 0.1820 2.35
0.0188
Year 0.5759 0.2849 2.02
0.0432
TS 0.6313 0.2750 2.30
0.0217
> validate(f,B=200)
index.orig training test optimism index.corrected n
Dxy 0.3861 0.4081 0.3699 0.0382 0.3479 200
R2 0.1537 0.1716 0.1378 0.0339 0.1198 200
Intercept 0.0000 0.0000 -0.0585 0.0585 -0.0585 200
Slope 1.0000 1.0000 0.8835 0.1165 0.8835 200
Emax 0.0000 0.0000 0.0375 0.0375 0.0375 200
D 0.1160 0.1315 0.1030 0.0285 0.0875 200
U -0.0063 -0.0063 0.0021 -0.0084 0.0021 200
Q 0.1223 0.1378 0.1010 0.0369 0.0855 200
B 0.2073 0.2035 0.2114 -0.0079 0.2153 200
g 0.8755 0.9415 0.8170 0.1244 0.7511 200
gp 0.1833 0.1920 0.1728 0.0192 0.1641 200
> plot.roc(RECU,l)
Call:
plot.roc.default(x = RECU, predictor = l)
Data: l in 201 controls (response 0) < 116 cases (response 1).
Area under the curve: 0.6931______________________________________________
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