From: Frank Harrell <f.harrell_at_vanderbilt.edu>

Date: Wed, 20 Apr 2011 06:01:11 -0700 (PDT)

> glm.family="binomial", OR20, strict=FALSE)

*> (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;
*

> [1] 136.8866

> [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...
*

*>
*

> glm.family="binomial", OR=20, strict=FALSE)

*> (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

Date: Wed, 20 Apr 2011 06:01:11 -0700 (PDT)

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)

> p!=0 EV SD model 1 model2

>> 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

>> BMAx6.glm <- bic.glm(postopDWI_HI ~ ., data=x6.df,

> glm.family="binomial", OR=20, strict=FALSE)

>> summary(BMAx6.glm)

> Call:

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. ______________________________________________ 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.Received on Wed 20 Apr 2011 - 13:04:52 GMT

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