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RE: st: RE: Ordinal logistic regression
From
Amal Khanolkar <[email protected]>
To
"[email protected]" <[email protected]>
Subject
RE: st: RE: Ordinal logistic regression
Date
Thu, 11 Nov 2010 17:23:19 +0100
An interesting discussion seems to be unfolding... :)
But to clear matters from my end: I've already checked associatiosn with continous BMI using linear regression. I just wanted to cross check associations with clinically important BMI cut-offs: overweight and obesity. And if mlogit ot ologit was the right choice or if something else suits this categorical-categorical associations.
/Amal.
Amal Khanolkar, PhD candidate,
Centre for Health Equity Studies (CHESS),
Karolinska Institutet,
106 91 Stockholm.
Ph# +46(0)8 162584/+46(0)73 0899409
www.chess.su.se
________________________________________
From: [email protected] [[email protected]] On Behalf Of Nick Cox [[email protected]]
Sent: 11 November 2010 17:20
To: '[email protected]'
Subject: RE: st: RE: Ordinal logistic regression
I sympathise with the idea, but that is a different issue.
If I wanted to forecast floods, I would use river discharge as a response, make quantitative predictions, and then the very last step is to see whether discharge means that the river is above some important threshold. Degrading my data to river discharge = {low, medium, high} at the outset is neither necessary nor helpful.
How does obesity differ?
Nick
[email protected]
Mary E. Mackesy-Amiti
I usually feel the same way about reducing information, but in some
cases the clinically-relevant categories are of greater interest than
the continuum.
On 11/11/2010 9:28 AM, Nick Cox wrote:
> Yes, but that strikes me as just throwing away information.
Amal Khanolkar
> I would like to know if BMI categorised into normal, overweight and obese could be considered as ordinal data and if so if be used as the outcome in 'ordinal logistic regression' with categorical exposures?
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