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Re: st: RE: regression assumption question
I suggest that Moleps get get a copy of
DR Bock, Multivariate Methods in Behavioral Research, 1975, McGraw
Hill, Section 7.3 (p 489+) or check the URL below. Bock's
description applies to the comparison of two groups. The following is
a sumary
If subjects were randomized to comparison groups (unlikely from
Molep's description), then both analyses estimate the same quantity,
but ANCOVA is more powerful.
If subjects were not randomized to groups, but can be considered as
randomly sampled, then ANCOVA and analysis of change scores estimate
different quantities. This is "Lord's Paradox. (Lord FM: A paradox in
the interpretation of group comparisons. Psychol Bull 1967, 68:304-5.
There is a nice discussion at: http://www.ete-online.com/content/
5/1/2#B21) .
With the proviso that subjects are drawn randomly from defined
populations (maybe hypothetical), the choice of analysis method
depends on the purpose of the study:
If the purpose is, in Bock's words, to "determine whether a
difference between the group mean final scores can be attributed to
the difference in baseline scores," use analysis of covariance.
If the purpose is to assess effects on changes, then analyze the
change scores.
As Austin states, problems arise because the baseline variable is
measured with error. There are other issues if, in addition,
assignment to comparison group or selection to follow-up was made on
the basis of a factor correlated with the baseline score.
-Steve
On Mar 10, 2009, at 1:01 PM, Austin Nichols wrote:
moleps islon:
I tend to agree with Tony <[email protected]>, but
note that if you believe that X affects the change in Y (dY = Ypost -
Ypre) but Y is measured with error pre and post, you may prefer not to
regress Ypost on Ypre and X, but rather to regress dY on X. Since
Ypre is measured with error, its coef may be biased toward zero and
you may be able to reject the null that its coef is one even when that
is the true model, and you may also have bias in other coefs when you
include Ypre, esp if treatment levels are correlated with true
baseline Y levels.
mat c=(1,0,0,0\ 0,1,0,0.5\ 0,0,1,0\ 0,0.5,0,1)
drawnorm e1 y0 e0 x, n(1000) corr(c) seed(1) clear
g ypost=y0+x/2+e1
g ypre=y0+e0
g dy=ypost-ypre
reg ypost ypre x, nohe r
reg dy x, nohe r
But of course if Ypre does not have a coef of one in the true model,
you will introduce bias by imposing that constraint in a regression of
dY on X. Nearest-neighbor matching (findit nnmatch) on pre-treatment
observables is another way forward here...
On Mon, Mar 9, 2009 at 3:58 PM, Lachenbruch, Peter
<[email protected]> wrote:
My preference is to use the preop measure as a covariate. If you use
the change, you are essentially forcing the preop to have a
coefficient
of 1. Sometimes people use the preop as a covariate for the change
score - this automatically induces a fairly high correlation with
preop
- if you're not careful, you can believe it.
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of moleps
islon
Sent: Monday, March 09, 2009 11:49 AM
To: [email protected]
Subject: st: regression assumption question
Dear listers,
I've got data on 300 patients preop and postop using the VAS scale
(ordinal scale). I'm trying to locate factors predicting improvement
postop. However there are several questions pertaining to this that
I'm unsure of. 1)Do I violate the assumption of independence? I
assume
there would be some correlation between preop and postop within the
patients. 2)Would you recommend using delta (preop-postop) as the
dependent variable or postop alone? The analyses so far show some
heteroscedasticity-in case i violate the independence assumption- is
it possible to do add both vce(robust) and vce(cluster id) ?
regards
Moleps
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