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st: time efficient way to choose variables


From   "Hardy, Dale S" <Dale.S.Hardy@uth.tmc.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: time efficient way to choose variables
Date   Tue, 3 Feb 2009 21:21:32 -0600

I have data in which I want to pick out variables associated with
developing a disease. Each time I run the foreach command with the
covariates, I cut out the one variable with the highest Z value with p
value <0.05, and I put this variable in the second equation (stcox)
until I have no variables with p value <0.05 left when I run the models
with the foreach command. 

Here is an example below:

foreach var of varlist agegrp racecode1 s_sex1 ses_pov ajcc6seer6_1
sizeband pnnumb grade_s lung4 comorbid treat2r xrt3 seer1 dxyear_cate {
stcox PAC1 `var`
}

Then I choose the variable with the  highest z score with p value <0.05
Then run the model again. Comorbid is taken out because of its highest Z
score and placed in the second equation.

foreach var of varlist agegrp racecode1 s_sex1 ses_pov ajcc6seer6_1
sizeband pnnumb grade_s lung4 treat2r xrt3 seer1 dxyear_cate {
stcox PAC1 comorbid  `var`
}

Third run:
Sizeband was chosen because of the highest Z score with p value <0.05
This was placed in the second model:

foreach var of varlist agegrp racecode1 s_sex1 ses_pov ajcc6seer6_1
pnnumb grade_s lung4 treat2r xrt3 seer1 dxyear_cate {
stcox PAC1 comorbid sizeband `var`
}

I do this until there is no more variables with p value <0.05 to choose
from.

1. My question is how can I do this process very quickly and time
efficient.
Do I use an array? Can you show me how to do this?

2. Is there also a time efficient process in looking for effect
modifiers using several variables (one at a time in separate models)
using the likelihood ratio test?


Thanks.



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