Peter and Maarten
I am sorry Peter. Your model is not accepted by Stata. I tried
different alternativ without success.
However I tried Maarten GLM model
xi: glm studytime i.drug c_age cons, family(gaussian) link(log) nocons eform
on my data and got a different result compared to the regress of the
lnLOS. Now laparoscopy has shorter LOS. Which method is correct?
OIM
LOS exp(b) Std. Err. z P>z [95% Conf. Interval]
lapscopiin~n .9564416 .0177129 -2.40 0.016 .9223473 .9917961
_Iappdgn2_1 1.877733 .0296501 39.90 0.000 1.82051 1.936755
_Iappdgn2_3 1.261943 .0406183 7.23 0.000 1.184792 1.344118
_Ialderkat_0 1.217428 .033424 7.17 0.000 1.153649 1.284732
_Ialderka~20 .9216973 .0248353 -3.03 0.002 .874284 .9716818
_Ialderka~30 .9769093 .0266964 -0.85 0.393 .9259619 1.03066
_Ialderka~40 1.042333 .0310393 1.39 0.164 .9832382 1.104979
_Ialderka~50 1.193937 .0329973 6.41 0.000 1.130984 1.260394
_Ialderka~60 1.288248 .0401942 8.12 0.000 1.211829 1.369486
_Ialderka~70 1.627175 .0499963 15.84 0.000 1.532076 1.728176
_Ialderka~80 2.187109 .0728674 23.49 0.000 2.048855 2.334693
prepermalign 1.309315 .0431838 8.17 0.000 1.227354 1.39675
precardios~s 1.055536 .0380158 1.50 0.133 .9835957 1.132739
preperlung~d 1.173726 .0379413 4.96 0.000 1.101669 1.250496
preperhype~i 1.031919 .0318163 1.02 0.308 .9714069 1.096201
preperhjar~t 1.217436 .049107 4.88 0.000 1.124894 1.31759
prepernjur~t 1.438939 .1225243 4.27 0.000 1.217765 1.700284
preperdiab~s 1.110856 .0446201 2.62 0.009 1.026756 1.201844
cons 2.244081 .0400108 45.33 0.000 2.167015 2.323887
Greetings
Roland
2008/11/5 Lachenbruch, Peter <[email protected]>:
> 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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