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From |
"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Interpretation of regressionmodel of ln-transformed variable |

Date |
Wed, 5 Nov 2008 08:44:29 -0800 |

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: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis Sent: Wednesday, November 05, 2008 1:49 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Interpretation of regressionmodel of ln-transformed variable --- roland andersson <rolandersson@gmail.com> 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/ ----------------------------------------- * * 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/ * * 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/

**Follow-Ups**:**Re: st: Interpretation of regressionmodel of ln-transformed variable***From:*"roland andersson" <rolandersson@gmail.com>

**References**:**Re: st: Interpretation of regressionmodel of ln-transformed variable***From:*"roland andersson" <rolandersson@gmail.com>

**Re: st: Interpretation of regressionmodel of ln-transformed variable***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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