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RE: st: Quantile regression


From   Vasan Kandaswamy <vasan.kandaswamy@ki.se>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Quantile regression
Date   Sat, 22 Sep 2012 20:09:28 +0000

Thank you Nick, JVerkuilen , David and all others for your suggestions and help. 
I am working as suggested and would get back in case I am stuck. 
Thanks for making it explicit and easy. 

Best regards,
Vasan

________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Nick Cox [njcoxstata@gmail.com]
Sent: Saturday, September 22, 2012 4:33 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Quantile regression

The choice need not seem so stark. With a link function approach, as
with -poisson- or -glm, link(log)-, you can fit on a log scale and
report on the original scale. I would guess that this would make most
sense to clinicians and patients alike in this example.

Nick

On Sat, Sep 22, 2012 at 2:09 PM, JVerkuilen (Gmail)
<jvverkuilen@gmail.com> wrote:
> Ah one more point:
>
> As to whether you use log(glucose) or glucose depends on the
> literature and what is a meaningful outcome. I cannot answer that, as
> this is really not my field, but one nice thing about quantile
> regression is that it has some nice invariance properties to monotone
> transformations of the dependent variable.
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