Austin
I will look into your answer in more detail later. I am not sure what
you mean by no right censoring. All the patients have been discharged
so there is no censoring.
And as to your final comment - this is real life data and the patients
are not randomised. The laparoscopic patients have shorter LOS if
described as the mean, but not as the median (LOS is median 2 days and
5-95% range 1-7 days in both groups). As there are confounding from
differences in age, diagnosis, sex, comorbidity and so forth the
laparoscopic patients may in fact have longer LOS after adjustment for
these confounders.
Greetings
Roland Andersson
2008/11/4 Austin Nichols <[email protected]>:
> 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 <[email protected]> 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 <[email protected]> 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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