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Re: st: stepwise


From   Timothy.Mak@iop.kcl.ac.uk
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: stepwise
Date   Mon, 4 Sep 2006 15:19:14 +0100

Hi Richard, 

Do you know how the SPSS pairwise procedure work? I don't think it works 
the way I wanted it to work. 

Now I'm really curious about why you suggested using a lower cut-off than 
.05. In fact I was going to use 0.15, as suggested in Hosmer and Lemeshow. 
I thought the point of a stepwise regression is to mimic what would happen 
if we could include every relevant regressor, so that the interpretation 
of the regression coefficient is something like the effect of A assuming 
everything else holds constant. I thought if we allow more predictors in, 
then these other predictors would approximate the 'everything else' better 
than if we only let the really significant ones in. If possible I would 
put everything in the equations, but for two concerns: 1. Some predictors 
simply do not go together - it doesn't make sense to say the effect of B 
controlled for C, for whatever reason. 2. The problem of having too many 
parameters in the asymptotic results of logistic regression. 

Perhaps some more background would help. In essence, we're trying to 
identify 'important' predictors of 'functional outcomes' of patients. We 
have 30 predictors from childhood up to current. I know that considerable 
literature exists for identifying 'important' covariates (eg the dominant 
analysis by Budescu (1993 - Psychological Bulletin)). But since the use of 
these techniques seems currently limited to specialist statistics journal, 
I thought I would go by the more traditional way of using simple and 
multiple regression. The pattern that I try to follow is from the 
following paper: Bienvenu et al (2006) British Journal of Psychiatry 
188:432. They do both simple and multiple logistic regression controlling 
for all of their predictors. They have a larger sample than we do and 
therefore they can fit 10-20 regressors in their regression. With our 
smaller sample, there's no way we'll put all 30 predictors into our model. 
My colleague ran a stepwise regression and it throws away about 40% of our 
data, which I'm not so pleased about. And therefore I'd like to know how 
PAIRWISE might help remedy the situation. Whether I use PAIRWISE or 
LISTWISE, I think it would probably be better if I run the regression 
again based on the selected variables over again. 

You are right that at the end of the day we won't be able to put much 
trust in the coefficients, but I feel it's still better than nothing. 

Thanks,

Tim





Richard Williams <Richard.A.Williams.5@ND.edu> 
Sent by: owner-statalist@hsphsun2.harvard.edu
04/09/2006 15:45
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Re: st: stepwise






At 04:30 AM 9/4/2006, Timothy.Mak@iop.kcl.ac.uk wrote:

>stepwise regression is needed. Say we have n = 200, and a potential pool
>of predictors = 50, say that each of these 50 predictors have 1 or 2
>missing, not necesarily randomly. Using the Stata stepwise procedure, we

Two other quick comments:  Since you are also using SPSS, you could
use pairwise deletion of missing data, which might be ok if the data
are missing randomly (but if nonrandom, you've got some problems
regardless of what you do).

Also, if you've got 50 predictors and are using the .05 level of
significance, then just by chance alone you'd expect 2 or 3 vars to
enter in.  I'd suggest using the .01 level of significance; or figure
out exactly what alpha level to use by doing, say, a Bonferroni 
adjustment.

Overall though, I probably wouldn't feel very comfortable with SW in
this case, regardless of how I did it!  I'd certainly want to add
some cautionary notes in my writeup, perhaps labeling the analysis as
exploratory and in need of replication in other studies.

I wonder if some of those 50 vars couldn't be combined into a smaller
number of scales?  Do you really have 50 unique concepts here, or do
you maybe have several items that tap into the same concept in
slightly different ways?


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Richard Williams, Notre Dame Dept of Sociology
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EMAIL:  Richard.A.Williams.5@ND.Edu
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