[R] BMA, logistic regression, odds ratio, model reduction etc
khosoda at med.kobe-u.ac.jp
khosoda at med.kobe-u.ac.jp
Wed Apr 20 16:44:04 CEST 2011
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.
>
> ______________________________________________
> 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.
--
*************************************************
神戸大学大学院医学研究科 脳神経外科学分野
細田 弘吉
〒650-0017 神戸市中央区楠町7丁目5-1
Phone: 078-382-5966
Fax : 078-382-5979
E-mail address
Office: khosoda at med.kobe-u.ac.jp
Home : khosoda at venus.dti.ne.jp
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