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:

[hidden email] [mailto:

[hidden email]] On Behalf Of Neil Shephard

Sent: Thursday, November 11, 2010 8:29 AM

To:

[hidden email]
Subject: Re: st: RE: Ordinal logistic regression

On Thu, Nov 11, 2010 at 4:13 PM, Mary E. Mackesy-Amiti <

[hidden email]> 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/CatContinuousOn Thu, Nov 11, 2010 at 4:20 PM, Nick Cox <

[hidden email]> 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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