On Dec 9, 2010, at 8:06 AM, 笑啸 wrote:
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,
As a doctor with some experience using logistic regression and looking
at the output of ROC analyses, I would ask why you think an AUC is
"not too bad"? Just flipping a fair coin would give you an expected
AUC of 0.5.
but the “Dxy”(I think it as the prediction accuracy probability) is
too low.
If I'm not mistaken there is a 1-1 relationship between AUC and Dxy,
so I suspect you have an incorrect scale for judging the merits of one
or the other of those figure of merit numbers. (You may want to
purchase Harrell's text "Regression Modeling Strategies" in which I
just checked my memory on this point and found that I was correct.)
--
David.
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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David Winsemius, MD
West Hartford, CT
______________________________________________
[email protected] mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
