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
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Maarten buis
Sent: Wednesday, November 05, 2008 1:49 AM
To: [email protected]
Subject: Re: st: Interpretation of regressionmodel of ln-transformed
variable
--- roland andersson <[email protected]> wrote:
> It is also difficult to imaging that there should be censoring
> for conditions that normally need 1 to 7 days of hospital visit.
Ok, sounds reasonable.
> Following your example I have made this model
>
> xi:regress lnLOS lapscopic i.appdgn age agesq cons, eform("exp(b)")
> nocons
>
> and get this result
>
> lnLOS exp(b) [95% Conf. Interval]
> lapscopic 1.018056 1.004532 1.031762
> _Iappdgn2_1 1.850726 1.824841 1.876978
> _Iappdgn2_3 1.174283 1.147247 1.201956
> age .9852508 .9841405 .9863623
> agesq 1.000275 1.000261 1.000289
> cons 2.208685 2.168225 2.2499
>
> I now understand that the exp(b) is a multiplicator, ie that open
> appendectomy has a geometric mean LOS of 2.21 days whereas
> laparoscopic patients have 1.02*2.21=2.25 days or 0.04 days longer
> geometric mean LOS. Is it correct to recalculate the CI of this
> difference as 2.21-1.0045*2.21=0.01 and 2.21-1.032*2.21=0.07?
In that case I would use -adjust- and -nlcom- like in the example
below:
*--------------- begin example --------------------------
sysuse cancer, clear
gen ln_t = ln(studytime)
gen cons = 1
xi: reg ln_t i.drug age cons, nocons eform("exp(b)")
adjust _Idrug_3=0 age, by(_Idrug_2) exp ci
sum age if e(sample)
nlcom exp((_b[cons] + _b[age]*`r(mean)')+ _b[_Idrug_2]) - ///
exp((_b[cons] + _b[age]*`r(mean)'))
*---------------- end example ---------------------------
Notice that the difference in LOS now depends on the values of the
other explanatory variables. These other variables define the baseline
LOS (in your case the LOS for someone who received an open
appendectomy). So if you haven't mean centered age, then the difference
in geometric mean LOS you reported applies to newly born babies. You
can report the difference in geometric mean LOS for someone of average
age either by first mean centering age (subtract the mean age from the
variable age as I did in the example in my previous post), or take mean
age into account like in the example above.
Hope this helps,
Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room N515
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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