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Re: st: Can Y be a predicted variable?


From   Tinna <statalist@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Can Y be a predicted variable?
Date   Fri, 9 Sep 2005 18:43:04 -0400

Thanks for the answers Constantine and Roger.  I am not sure it
matters now that my "genius" idea has been shot down, but let me still
answer your question just for fun :)

Question: why "average" the observed and predicted? What is the rationale for
that and what do you get out of it?

Answer: There was no special thought in giving the values 50-50
weight, but the idea behind averaging was to keep the scale largely
the same as this 5 level SAHS, which is very widely used and easy to
understand. I might as well have just added without dividing by two,
but then the scale would have been different.

Thanks
Tina




On 9/9/05, Constantine Daskalakis <C_Daskalakis@mail.jci.tju.edu> wrote:
> At 03:45 PM 9/9/2005, Tinna wrote:
> >So will I get fried if I do it my proposed way, or will the results
> >just be difficult to read for non-statisticians.
> >Tina
> 
> > > At 18:31 09/09/2005, Tina wrote:
> > > >Dear statalisters,
> > > >
> > > >I have a dependent variable in 5 levels (Self-Assessed Health Status
> > > >from very good to very poor). I am currently assuming a latent
> > > >continuous variable, but that is problematic for some of my analysis.
> > > >I have some other measures of health in my data and was wondering if
> > > >it was appropriate to create a new one that would be continuous. My
> > > >suggestion would be:
> > > >
> > > >1. regress SAHS on other health variables.
> > > >2. Predict SAHS (lets call it SAHShat) based on the previous regression.
> > > >3. The new measure would be calculated as an average of SAHS and SAHShat
> > > >
> > > >This looks like a good idea to me, but I wonder why I don't see anyone
> > > >else doing this if it is OK. Those of you that fell of your office
> > > >chairs in laughter could maybe get back on and explain why not,
> > > >because it seems fine idea to me right now.
> > >
> 
> 
> I am not laughing, but I think there are plenty of reasons why this is NOT
> done.
> 
> For one, the "predicted" variable (SAHShat) is not an observed outcome but
> one that you have "imputed" (via your regression of SAHS on other health
> variables). How good the prediction is can be debatable. And what beast
> this "predicted" variable really is can be equally debatable.
> 
> Second, it is not clear how you would even regress the ordinal SAHS
> variable on other variables. Through some ordinal regression model? Then,
> your predicted values would still be discrete. Through some linear
> regression model? But if your original SAHS variable were continuous enough
> for that, you wouldn't worry in the first place.
> 
> Third, the fact that those values are imputed rather than observed adds
> variability that will not be accounted for in your main analyses. Usual
> methods take Y to be an actually observed outcome.
> 
> Fourth, why "average" the observed and predicted? What is the rationale for
> that and what do you get out of it?
> 
> 
> In addition to Roger's suggestions, another avenue might be to actually do
> a formal latent variable analysis, where you would use SAHS (and possibly
> other covariates) as proxies of your unobserved latent outcome. I think
> structural equations come into this but it is not my field.
> 
> 
> Constantine
> 
> 
> 
> 
> 
> The documents accompanying this transmission may contain confidential
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> 
> ________________________________________________________________
> Constantine Daskalakis, ScD
> Assistant Professor,
> Thomas Jefferson University, Division of Biostatistics,
> 211 S. 9th St., Suite 602, Philadelphia, PA 19107
>    Tel: 215-955-5695
>    Fax: 215-503-3804
>    Email: c_daskalakis@mail.jci.tju.edu
>    Webpage: http://www.jefferson.edu/clinpharm/bio/
> 
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