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st: Interpretation of regressionmodel of ln-transformed variable


From   "roland andersson" <rolandersson@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Interpretation of regressionmodel of ln-transformed variable
Date   Mon, 3 Nov 2008 22:32:13 +0100

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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