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
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