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


From   "Austin Nichols" <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Interpretation of regressionmodel of ln-transformed variable
Date   Tue, 4 Nov 2008 13:12:34 -0500

roland andersson :

If you think E(LOS) is a function exp(Xb) of X, and you have no right
censoring (LOS is known only to exceed current measured stay length),
then yes, you can use -poisson- or -glm- but you would want to (at
least) specify the robust option to get het-robust SEs, I think.  But
you should get very similar coefficients/marginal effects unless a
large number of important cases have zero LOS, and get dropped by the
log transformation.

use http://fmwww.bc.edu/RePEc/bocode/i/ivp_bwt.dta, clear
poisson bw cigspreg parity white male, r
mfx
glm bw cigspreg parity white male, link(log) r
mfx
glm bw cigspreg parity white male, link(log) r fam(gamma)
mfx
g lnbw=ln(bw)
reg lnbw cigspreg parity white male, r

See also discussion (and refs) in the help file for ivpois:
 ssc install ivpois, replace

Do you think laparoscopic surgery is randomly assigned in your data?

On Mon, Nov 3, 2008 at 4:52 PM, Maarten buis <maartenbuis@yahoo.co.uk> wrote:
> The way to model length of stay data is to use survival analysis and
> not to use -regress-. Some online resources for learning about that
> are:
> http://www.iser.essex.ac.uk/teaching/degree/stephenj/ec968/
> http://www.ats.ucla.edu/stat/stata/seminars/stata_survival/default.htm
> http://home.fsw.vu.nl/m.buis/wp/survival.html
>
> regarding the interpretation of a -regress- model after transforming
> the dependent variable see:
> http://www.stata.com/statalist/archive/2008-11/msg00039.html
> http://www.stata.com/statalist/archive/2008-10/msg01362.html
> http://www.stata.com/statalist/archive/2008-10/msg01364.html
>
> Hope this helps,
> Maarten
>
> --- roland andersson <rolandersson@gmail.com> wrote:
>
>> I am analysisng the impact of laparoscopic surgery on hospital length
>> of stay (LOS). The LOS is skewed and the median and 5-95 percentil
>> range is exactly the same for laparoscopic and open surgery. The
>> Mann-Whitney test is non significant.
>>
>> I want to model the LOS with some confounders (diagnosis at
>> operation,
>> sex, comorbidity, age). I have used linear regression on the
>> ln-transformed LOS
>>
>> lnLOS         Coef.            Std. Err.       t      P>t              [95% Conf.
>> Interval]
>> lapscopic     .0023183        .0070385        0.33    0.742   -.0114774       .0161141
>> snip
>> a number of covariates
>> snip
>> _cons .7079673        .0127527        55.52   0.000   .6829717        .7329628
>>
>> How can I revert the result of the linear regresion of ln-transformed
>> LOS to difference between laparoscopic and open in days? Exp(0.002)
>> gives 1.002 but this can not represent the difference between the
>> laparoscopic and open surgical methods.
>>
>> Somewhere on the statalist I have read that poissonregression can be
>> used in this situation. This is the result of a poissonregression:
>>
>> LOS           Coef.   Std. Err.       z       P>z             [95% Conf.      Interval]
>> lapscopic             -.0225546       .0075029        -3.01   0.003   -.03726 -.0078492
>> snip
>> a number of covariates
>> snip
>> _cons .986855 .0131518        75.04   0.000   .9610779        1.012632
>>
>> How do I interepret this result? Is the laparoscopic LOS
>> significantly
>> shorter with 0.02 days?
>>
>> I would appreciate your help.
>>
>> Regards
>> Roland Andersson
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