[R] Questions about lrm, validate, pentrace (Re: BMA, logistic regression, odds ratio, model reduction etc)
khosoda at med.kobe-u.ac.jp
khosoda at med.kobe-u.ac.jp
Sat Apr 23 17:53:40 CEST 2011
According to the advice, I tried rms package.
Just to make sure, I have data of 104 patients (x6.df), which consists
of 5 explanatory variables and one binary outcome (poor/good) (previous
model 2 strategy). The outcome consists of 25 poor results and 79 good
results. Therefore, My events per variable (EPV) is only 5 (much less
than the rule of thumb of 10).
My questions are about validate and pentrace in rms package.
I present some codes and results.
I appreciate anybody's help in advance.
> x6.lrm <- lrm(outcome ~ stenosis+x1+x2+procedure+ClinicalScore,
data=x6.df, x=T, y=T)
> x6.lrm
...
Obs 104 LR chi2 29.24 R2 0.367 C 0.816
negative 79 d.f. 5 g 1.633 Dxy 0.632
positive 25 Pr(> chi2) <0.0001 gr 5.118 gamma 0.632
max |deriv| 1e-08 gp 0.237 tau-a 0.233
Brier 0.127
Coef S.E. Wald Z Pr(>|Z|)
Intercept -5.5328 2.6287 -2.10 0.0353
stenosis -0.0150 0.0284 -0.53 0.5979
x1 3.0425 0.9100 3.34 0.0008
x2 -0.7534 0.4519 -1.67 0.0955
procedure 1.2085 0.5717 2.11 0.0345
ClinicalScore 0.3762 0.2287 1.65 0.0999
It seems not too bad. Next, validation by bootstrap ...
> validate(x6.lrm, B=200, bw=F)
index.orig training test optimism index.corrected n
Dxy 0.6324 0.6960 0.5870 0.1091 0.5233 200
R2 0.3668 0.4370 0.3154 0.1216 0.2453 200
Intercept 0.0000 0.0000 -0.2007 0.2007 -0.2007 200
Slope 1.0000 1.0000 0.7565 0.2435 0.7565 200
Emax 0.0000 0.0000 0.0999 0.0999 0.0999 200
D 0.2716 0.3368 0.2275 0.1093 0.1623 200
U -0.0192 -0.0192 0.0369 -0.0561 0.0369 200
Q 0.2908 0.3560 0.1906 0.1654 0.1254 200
B 0.1272 0.1155 0.1384 -0.0229 0.1501 200
g 1.6328 2.0740 1.4647 0.6093 1.0235 200
gp 0.2367 0.2529 0.2189 0.0341 0.2026 200
The apparent Dxy is 0.63, and bias-corrected Dxy is 0.52. The maximum
absolute error is estimated to be 0.099. The changes in slope and
intercept are also more substantial. In all, there is evidence that I am
somewhat overfitting the data, right?.
Furthermore, using step-down variable selection ...
> validate(x6.lrm, B=200, bw=T)
Backwards Step-down - Original Model
Deleted Chi-Sq d.f. P Residual d.f. P AIC
stenosis 0.28 1 0.5979 0.28 1 0.5979 -1.72
ClinicalScore 2.60 1 0.1068 2.88 2 0.2370 -1.12
x2 2.86 1 0.0910 5.74 3 0.1252 -0.26
Approximate Estimates after Deleting Factors
Coef S.E. Wald Z P
Intercept -5.865 1.4136 -4.149 3.336e-05
x1 2.915 0.8685 3.357 7.889e-04
procedure 1.072 0.5590 1.918 5.508e-02
Factors in Final Model
[1] x1 procedure
index.orig training test optimism index.corrected n
Dxy 0.5661 0.6755 0.5559 0.1196 0.4464 200
R2 0.2876 0.4085 0.2784 0.1301 0.1575 200
Intercept 0.0000 0.0000 -0.2459 0.2459 -0.2459 200
Slope 1.0000 1.0000 0.7300 0.2700 0.7300 200
Emax 0.0000 0.0000 0.1173 0.1173 0.1173 200
D 0.2038 0.3130 0.1970 0.1160 0.0877 200
U -0.0192 -0.0192 0.0382 -0.0574 0.0382 200
Q 0.2230 0.3323 0.1589 0.1734 0.0496 200
B 0.1441 0.1192 0.1452 -0.0261 0.1702 200
g 1.2628 1.9524 1.3222 0.6302 0.6326 199
gp 0.2041 0.2430 0.2043 0.0387 0.1654 199
If I select only two variables (x1 and procedure), bias-corrected Dxy
goes down to 0.45.
[Question 1]
I have EPV problem. Even so, should I keep the full model (5-variable
model)? or can I use the 2-variable (x1 and procedure) model which the
validate() with step-down provides?
[Question 2]
If I use 2-variable model, should I do
x2.lrm <- lrm(postopDWI_HI ~ T1+procedure2, data=x6.df, x=T, y=T)?
or keep the value showed above by validate function?
Next, shrinkage ...
> pentrace(x6.lrm, seq(0, 5.0, by=0.05))
Best penalty:
penalty df
3.05 4.015378
The best penalty is 3.05. So, I update it with this penalty to obtain
the corresponding penalized model:
> x6.lrm.pen <- update(x6.lrm, penalty=3.05, x=T, y=T)
> x6.lrm.pen
.....
Penalty factors
simple nonlinear interaction nonlinear.interaction
3.05 3.05 3.05 3.05
Final penalty on -2 log L
[,1]
[1,] 3.8
Obs 104 LR chi2 28.18 R2 0.313 C 0.818
negative 79 d.f. 4.015 g 1.264 Dxy 0.635
positive 25 Pr(> chi2) <0.0001 gr 3.538 gamma 0.637
max |deriv| 3e-05 gp 0.201 tau-a 0.234
Brier 0.129
Coef S.E. Wald Z Pr(>|Z|) Penalty Scale
Intercept -4.7246 2.2429 -2.11 0.0352 0.0000
stenosis -0.0105 0.0240 -0.44 0.6621 17.8021
x1 2.3605 0.7254 3.25 0.0011 0.6054
x2 -0.5385 0.3653 -1.47 0.1404 1.2851
procedure 0.9247 0.4844 1.91 0.0563 0.8576
ClinicalScore 0.3046 0.1874 1.63 0.1041 2.4779
Arrange the coefficients of the two models side by side, and also list
the difference between the two:
> cbind(coef(x6.lrm), coef(x6.lrm.pen), abs(coef(x6.lrm)-coef(x6.lrm.pen)))
[,1] [,2] [,3]
Intercept -5.53281808 -4.72464766 0.808170417
stenosis -0.01496757 -0.01050797 0.004459599
x1 3.04248257 2.36051833 0.681964238
x2 -0.75335619 -0.53854750 0.214808685
procedure 1.20847252 0.92474708 0.283725441
ClinicalScore 0.37623189 0.30457557 0.071656322
[Question 3]
Is this penalized model the one I should present for my colleagues?
I still have EPV problem. Or is EPV problem O.K. if I use penalization?
I am still wondering about what I can do to avoid EPV problem.
Collecting new data would be a long-time and huge work...
(11/04/22 1:46), khosoda at med.kobe-u.ac.jp wrote:
> Thank you for your comment.
> I forgot to mention that varclus and pvclust showed similar results for
> my data.
>
> BTW, I did not realize rms is a replacement for the Design package.
> I appreciate your suggestion.
> --
> KH
>
> (11/04/21 8:00), Frank Harrell wrote:
>> I think it's OK. You can also use the Hmisc package's varclus function.
>> Frank
>>
>>
>> 細田弘吉 wrote:
>>>
>>> Dear Prof. Harrel,
>>>
>>> Thank you very much for your quick advice.
>>> I will try rms package.
>>>
>>> Regarding model reduction, is my model 2 method (clustering and recoding
>>> that are blinded to the outcome) permissible?
>>>
>>> Sincerely,
>>>
>>> --
>>> KH
>>>
>>> (11/04/20 22:01), Frank Harrell wrote:
>>>> Deleting variables is a bad idea unless you make that a formal part of
>>>> the
>>>> BMA so that the attempt to delete variables is penalized for.
>>>> Instead of
>>>> BMA I recommend simple penalized maximum likelihood estimation (see the
>>>> lrm
>>>> function in the rms package) or pre-modeling data reduction that is
>>>> blinded
>>>> to the outcome variable.
>>>> Frank
>>>>
>>>>
>>>> 細田弘吉 wrote:
>>>>>
>>>>> Hi everybody,
>>>>> I apologize for long mail in advance.
>>>>>
>>>>> I have data of 104 patients, which consists of 15 explanatory
>>>>> variables
>>>>> and one binary outcome (poor/good). The outcome consists of 25 poor
>>>>> results and 79 good results. I tried to analyze the data with logistic
>>>>> regression. However, the 15 variables and 25 events means events per
>>>>> variable (EPV) is much less than 10 (rule of thumb). Therefore, I
>>>>> used R
>>>>> package, "BMA" to perform logistic regression with BMA to avoid this
>>>>> problem.
>>>>>
>>>>> model 1 (full model):
>>>>> x1, x2, x3, x4 are continuous variables and others are binary data.
>>>>>
>>>>>> x16.bic.glm<- bic.glm(outcome ~ ., data=x16.df,
>>>>> glm.family="binomial", OR20, strict=FALSE)
>>>>>> summary(x16.bic.glm)
>>>>> (The output below has been cut off at the right edge to save space)
>>>>>
>>>>> 62 models were selected
>>>>> Best 5 models (cumulative posterior probability = 0.3606 ):
>>>>>
>>>>> p!=0 EV SD model 1 model2
>>>>> Intercept 100 -5.1348545 1.652424 -4.4688 -5.15
>>>>> -5.1536
>>>>> age 3.3 0.0001634 0.007258 .
>>>>> sex 4.0
>>>>> .M -0.0243145 0.220314 .
>>>>> side 10.8
>>>>> .R 0.0811227 0.301233 .
>>>>> procedure 46.9 -0.5356894 0.685148 . -1.163
>>>>> symptom 3.8 -0.0099438 0.129690 . .
>>>>> stenosis 3.4 -0.0003343 0.005254 .
>>>>> x1 3.7 -0.0061451 0.144084 .
>>>>> x2 100.0 3.1707661 0.892034 3.2221 3.11
>>>>> x3 51.3 -0.4577885 0.551466 -0.9154 .
>>>>> HT 4.6
>>>>> .positive 0.0199299 0.161769 . .
>>>>> DM 3.3
>>>>> .positive -0.0019986 0.105910 . .
>>>>> IHD 3.5
>>>>> .positive 0.0077626 0.122593 . .
>>>>> smoking 9.1
>>>>> .positive 0.0611779 0.258402 . .
>>>>> hyperlipidemia 16.0
>>>>> .positive 0.1784293 0.512058 . .
>>>>> x4 8.2 0.0607398 0.267501 . .
>>>>>
>>>>>
>>>>> nVar 2 2
>>>>> 1 3 3
>>>>> BIC -376.9082
>>>>> -376.5588 -376.3094 -375.8468 -374.5582
>>>>> post prob 0.104
>>>>> 0.087 0.077 0.061 0.032
>>>>>
>>>>> [Question 1]
>>>>> Is it O.K to calculate odds ratio and its 95% confidence interval from
>>>>> "EV" (posterior distribution mean) and“SD”(posterior distribution
>>>>> standard deviation)?
>>>>> For example, 95%CI of EV of x2 can be calculated as;
>>>>>> exp(3.1707661)
>>>>> [1] 23.82573 -----> odds ratio
>>>>>> exp(3.1707661+1.96*0.892034)
>>>>> [1] 136.8866
>>>>>> exp(3.1707661-1.96*0.892034)
>>>>> [1] 4.146976
>>>>> ------------------> 95%CI (4.1 to 136.9)
>>>>> Is this O.K.?
>>>>>
>>>>> [Question 2]
>>>>> Is it permissible to delete variables with small value of "p!=0" and
>>>>> "EV", such as age (3.3% and 0.0001634) to reduce the number of
>>>>> explanatory variables and reconstruct new model without those
>>>>> variables
>>>>> for new session of BMA?
>>>>>
>>>>> model 2 (reduced model):
>>>>> I used R package, "pvclust", to reduce the model. The result suggested
>>>>> x1, x2 and x4 belonged to the same cluster, so I picked up only x2.
>>>>> Based on the subject knowledge, I made a simple unweighted sum, by
>>>>> counting the number of clinical features. For 9 features (sex, side,
>>>>> HT2, hyperlipidemia, DM, IHD, smoking, symptom, age), the sum ranges
>>>>> from 0 to 9. This score was defined as ClinicalScore. Consequently, I
>>>>> made up new data set (x6.df), which consists of 5 variables (stenosis,
>>>>> x2, x3, procedure, and ClinicalScore) and one binary outcome
>>>>> (poor/good). Then, for alternative BMA session...
>>>>>
>>>>>> BMAx6.glm<- bic.glm(postopDWI_HI ~ ., data=x6.df,
>>>>> glm.family="binomial", OR=20, strict=FALSE)
>>>>>> summary(BMAx6.glm)
>>>>> (The output below has been cut off at the right edge to save space)
>>>>> Call:
>>>>> bic.glm.formula(f = postopDWI_HI ~ ., data = x6.df, glm.family =
>>>>> "binomial", strict = FALSE, OR = 20)
>>>>>
>>>>>
>>>>> 13 models were selected
>>>>> Best 5 models (cumulative posterior probability = 0.7626 ):
>>>>>
>>>>> p!=0 EV SD model 1 model 2
>>>>> Intercept 100 -5.6918362 1.81220 -4.4688 -6.3166
>>>>> stenosis 8.1 -0.0008417 0.00815 . .
>>>>> x2 100.0 3.0606165 0.87765 3.2221 3.1154
>>>>> x3 46.5 -0.3998864 0.52688 -0.9154 .
>>>>> procedure 49.3 0.5747013 0.70164 . 1.1631
>>>>> ClinicalScore 27.1 0.0966633 0.19645 . .
>>>>>
>>>>>
>>>>> nVar 2 2 1
>>>>> 3 3
>>>>> BIC -376.9082 -376.5588
>>>>> -376.3094 -375.8468 -375.5025
>>>>> post prob 0.208 0.175
>>>>> 0.154 0.122 0.103
>>>>>
>>>>> [Question 3]
>>>>> Am I doing it correctly or not?
>>>>> I mean this kind of model reduction is permissible for BMA?
>>>>>
>>>>> [Question 4]
>>>>> I still have 5 variables, which violates the rule of thumb, "EPV> 10".
>>>>> Is it permissible to delete "stenosis" variable because of small value
>>>>> of "EV"? Or is it O.K. because this is BMA?
>>>>>
>>>>> Sorry for long post.
>>>>>
>>>>> I appreciate your help very much in advance.
>>>>>
>>>>> --
>>>>> KH
>>>>>
>>>>> ______________________________________________
>>>>> R-help at r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>>> PLEASE do read the posting guide
>>>>> http://www.R-project.org/posting-guide.html
>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>
>>>>
>>>>
>>>> -----
>>>> Frank Harrell
>>>> Department of Biostatistics, Vanderbilt University
>>>> --
>>>> View this message in context:
>>>> http://r.789695.n4.nabble.com/BMA-logistic-regression-odds-ratio-model-reduction-etc-tp3462416p3462919.html
>>>>
>>>> Sent from the R help mailing list archive at Nabble.com.
>>>>
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