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
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
