[R] error in lrm( )

[笑啸]

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