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RE: st: Interpretation of regressionmodel of ln-transformed variable


From   "Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Interpretation of regressionmodel of ln-transformed variable
Date   Thu, 6 Nov 2008 08:11:23 -0800

You need a space between power and -1
Check the help for glm

Tony

Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of roland
andersson
Sent: Wednesday, November 05, 2008 10:54 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Interpretation of regressionmodel of ln-transformed
variable

I tried the model

xi: glm lengthof stay lapscopy i.appdgn2 i.alderk prepermalign
precardioscleros  preperdiabetes cons, eform link(power-1) nocons
and get an error message "unrecognized command:  power"

Roland

2008/11/5 Nick Cox <n.j.cox@durham.ac.uk>:
> I doubt that anything is wrong with Tony's model except that
> -eform("exp(b)")- should just be -eform-.
>
> Nick
>
> roland andersson
>
> Peter and Maarten
>
> I am sorry Peter. Your model is not accepted by Stata. I tried
> different alternativ without success.
>
> However I tried Maarten GLM model
>
> xi: glm studytime i.drug c_age cons, family(gaussian) link(log) nocons
> eform
>
> on my data and got a different result compared to the regress of the
> lnLOS. Now laparoscopy has shorter LOS. Which method is correct?
>
>
> 2008/11/5 Lachenbruch, Peter <Peter.Lachenbruch@oregonstate.edu>:
>
>> The issue seems to be that hospitals have a closure date on stay when
>> you are doing a study after patients are certain (or almost certain)
> to
>> have been discharged (e.g., all records are from admissions at least
a
>> year old).
>>
>> An alternative model might fit the reciprocal of the mean rather than
>> the log of the observations (thus obviating problems with 0 days of
> stay
>> - e.g. an outpatient visit to the ER)  in this case you could use
>> generalized linear models to get
>> xi: glm LOS  lapscopic i.appdgn age agesq cons, eform("exp(b)")
>> link(power -1) nocons
>>
>>
>> Tony
>
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