[R-sig-ME] geometric mean regression

[Cole, Tim]

Hi Kevin and Ahmad,

Back transformation is not tricky on the natural log scale. Just multiply the coefficients by 100 and view them as differences in percentage units – see https://doi.org/10.1136/bmj.j3683s .

Best wishes,
Tim
--
 mailto:tim.cole at ucl.ac.uk Phone 020 7905 2666
Population Policy and Practice Programme
UCL Great Ormond Street Institute of Child Health, 
30 Guilford Street, London WC1N 1EH, UK

Date: Sun, 1 Apr 2018 13:14:16 -0400
From: "Kevin E. Thorpe" <mailto:kevin.thorpe at utoronto.ca>
To: Ahmad <mailto:ahmadr215 at tpg.com.au>
Cc: <mailto:r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] geometric mean regression
Message-ID: <mailto:bd0017d4-17b5-be6e-f7d0-0a32979a9fa1 at utoronto.ca>
Content-Type: text/plain; charset="utf-8"; Format="flowed"

Back transformation can be tricky. You should also look at smearing 
estimators. The package Hmisc has a function called smearingEst() that 
you might like to check.

Kevin

On 03/31/2018 06:37 PM, Ahmad wrote:
Hi Kevin
Thanks for your email,
Yes, I almost figured out how get this done. I needed to get the exp() of intercept for the reference group and exp() of coefficient*exp(intercept) for the other group.
When I was trying this for geometric of 95%CI, the results don't seem quite right. I found an article that if I get the exp() of lsmeans (emmeans) these will produce the correct geometric outputs. Not sure why when I do these manually using the exp() of intercept and coefficient of lm- the outputs are not identical, but close enough.
Ahmad
       
-----Original Message-----
From: Kevin E. Thorpe <mailto:kevin.thorpe at utoronto.ca>
Sent: Sunday, 1 April 2018 12:22 AM
To: Ahmad <mailto:ahmadr215 at tpg.com.au>
Subject: Re: [R-sig-ME] geometric mean regression
Maybe I'm missing something, but doesn't linear regression on log(y) accomplish this?
Kevin
On 03/29/2018 08:25 AM, Ahmad wrote:
Hi All

I have a dataset and I have been asked to generate geometric means
from the linear regression for different groups (2 groups).

In fact my data is repeated measures, and I intend to use a
mixed-effects regression model with repeated measures. But I thought I
can learn how to do this for a simple geometric mean regression, I
should be able to translate this into a mixed model.

Any help would be greatly appreciated!

Thanks

Ahmad