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re: Re: st: RE: Ordinal logistic regression


From   "Seed, Paul" <[email protected]>
To   "[email protected]" <[email protected]>
Subject   re: Re: st: RE: Ordinal logistic regression
Date   Fri, 12 Nov 2010 19:31:04 +0000

Dear all, 

I tried out the links Neil suggested.
As expected, dichotomizing generally lead to a loss of power.
However, when it did not, this was due to outliers (in y and x).
AS BMI is susceptible to occasional genuine extreme outliers,
there is some sort of argument for dichotomizing.

Another argument would be the provision of results in a familiar 
form for clinicians, even at the expense of loss of power. (There 
are WHO guidelines for BMI cutoffs, which are in clinical use).

False outliers in BMI, due to confusing pounds & kg or inches 
and cm are another matter...

BW

Paul Seed


From: [email protected] [mailto:[email protected]] On Behalf Of Neil Shephard
Sent: Thursday, November 11, 2010 8:29 AM
To: [email protected]
Subject: Re: st: RE: Ordinal logistic regression

On Thu, Nov 11, 2010 at 4:13 PM, Mary E. Mackesy-Amiti <[email protected]> wrote:
> I usually feel the same way about reducing information, but in some cases
> the clinically-relevant categories are of greater interest than the
> continuum.

Its completely arbitrary though.  Besides which BMI isn't a robust
indicator of obesity as it doesn't work for people who are very fit
and have lots of well honed muscles (their BMI often puts them in the
"obese" category when they are anything but).

Plenty of information on why not to categorise continuous variables at
http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/CatContinuous

On Thu, Nov 11, 2010 at 4:20 PM, Nick Cox <[email protected]> wrote:
> How does obesity differ?

It doesn't, but clinicians seem to struggle with these concepts.

Neil
- -- 
"Our civilization would be pitifully immature without the intellectual
revolution led by Darwin" - Motoo Kimura, The Neutral Theory of
Molecular Evolution

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