That is a very good point by Ronan! I overlooked in my initial response that
you had a dummy as explanatory variable where the -beta- option to -regress-
does not make sense. Note that the calculation you envisage toward the end
of your post is easily accomplished via -egen, std-.
HTH
Martin
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Ronan Conroy
Sent: Thursday, September 11, 2008 2:08 PM
To: [email protected]
Subject: Re: st: per standard deviation change
On 11 Sep 2008, at 12:35, Mohammed El Faramawi wrote:
> I want to ask about modeling a continuous variable as per standard
> deviation change. For example if I have a continuous variable such
> as arterial blood pressure(BP) measured in mmhg and I want to
> express the relationship between per standard deviation change of
> blood pressure and the physical activity (coded yes, no). How can I
> do that? Should I calculate the mean of BP abd then subtract it from
> the individual observation then divide the product by standard
> deviation.
You are making life too complicated! Here's an example with mean
arterial pressure, measured in women, late in pregnancy:
. regress map smoking if visit==5
Source | SS df MS Number of obs
= 256
-------------+------------------------------ F( 1, 254)
= 8.35
Model | 788.039627 1 788.039627 Prob > F
= 0.0042
Residual | 23975.3197 254 94.3910226 R-squared
= 0.0318
-------------+------------------------------ Adj R-squared
= 0.0280
Total | 24763.3594 255 97.1112132 Root MSE
= 9.7155
----------------------------------------------------------------------------
--
map | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
smoking | -3.562013 1.232783 -2.89 0.004 -5.989791
-1.134234
_cons | 93.37333 .7932676 117.71 0.000 91.81111
94.93555
----------------------------------------------------------------------------
--
You can see that smoking reduces the mean arterial pressure by 3.6 mm/
Hg (the coefficient for smoking) with a confidence interval of 1.1 to
6.0 (5.989791 rounded). This is an important reason why smokers'
babies are born lighter than non-smokers' (they lose the equivalent to
one week of gestational development).
Reporting the effect thus has the advantage that a clinician can
understand it, because it is expressed in the units in which blood
pressure is measured. And from your point of view, the advantage is
that you can extract this information very easily from -regress-.
You can do likewise with your exercisers versus non-exercisers
comparison.
No-one understands standard deviations in clinical medicine.
>
> Or should I subtract the standard deviation from the individual
> observation?
> Also, Should I take the absolute variable when the new variable is
> created or should I keep the signs when the regression is conducted?
You will notice how I reported the effect size and its confidence
interval with the minus signs removed. As a general rule, no-one
understands negative numbers and no-one understands risk ratios or
odds ratios that are less than one.
Ronan Conroy
=================================
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Royal College of Surgeons in Ireland
Epidemiology Department,
Beaux Lane House, Dublin 2, Ireland
+353 (0)1 402 2431
+353 (0)87 799 97 95
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P Before printing, think about the environment
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