[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"roland andersson" <rolandersson@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Wed, 5 Nov 2008 08:32:43 +0100 |

Maarten All patients have a date when they entered the hospital and another date of discharge. I think this is the normal for this type of data. I have not heard about hospital length of stay where there are censored data. It is also difficult to imaging that there should be censoring for conditions that normally need 1 to 7 days of hospital visit. Anyhow thank you for the examples which I have tried out. It certainly helps me understand. I still need your help how to interpret and express the result. What I need is to express the difference in LOS in days between open and laparoscopic surgery after adjustment for confounders. I am sorry but it would be too complicated to express the length of stay as a hazard or as an IRR. 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) Std. Err. t P>t [95% Conf. Interval] lapscopic 1.018056 .006946 2.62 0.009 1.004532 1.031762 _Iappdgn2_1 1.850726 .0132997 85.66 0.000 1.824841 1.876978 _Iappdgn2_3 1.174283 .013955 13.52 0.000 1.147247 1.201956 age .9852508 .0005668 -25.83 0.000 .9841405 .9863623 agesq 1.000275 7.26e-06 37.90 0.000 1.000261 1.000289 cons 2.208685 .0208339 84.01 0.000 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? Greetings Roland Andersson, MD PhD 2008/11/4 Maarten buis <maartenbuis@yahoo.co.uk>: > You claim that there is no right censoring. That is very unusual for > this type of data. To me that suggests that some pre-processing of the > data took place in which the censored cases were deleted, which would > be bad as you will than be left with a rather selective subsample. > > Anyhow, in any of these models you are not looking for the difference > in days but the ratio of geometric mean length of stays (-regress-), or > ratios of arithmatic mean length of stays (-glm- or -poisson-), or the > ratio of the hazard of leaving hospital (-stcox-). This is best > explained using an example. > > In example 1 below you can see that in the first example the geometric > mean "LOS" (actually in this example time till death) for someone of > average age and getting drug 1 is 6.63 months (cons), while this > geometric mean LOS increases by a factor 2.11 when the individual > receives drug 1 and by a factor 3.22 if the individual receives drug 3, > but the time decreased by a factor .946 (that is, it decreases by > 100*(1-.946) = 5.4%) when the individual gets a year older > > In example 2 below you can see that the arithmetic mean LOS for someone > of average age and getting drug 1 is 9.21 (-glm-) or 8.92 (-poisson-), > while the arithmetic mean LOS increases by a 1.67 (-glm-) or 1.69 > (-poisson-) when the individual receives drug 2. > > In example 3 I don't show the baseline hazard (the hazard for the > individual with mean age and receiving drug 1) as Cox regression is > specifically designed to leave the baseline hazard unspecified (which > is why it is sometimes called a semi-parametric technique). It does > show that the hazard now decreases by a factor .18 (that is the hazard > of someone with drug 2 is 82% less than the hazard for someone > receiving drug 1) > > *------------------ begin example -------------------- > // some data preparation > sysuse cancer, clear > gen ln_t = ln(studytime) > sum age > gen c_age = age-r(mean) > gen cons = 1 > > // example 1: > xi: reg ln_t i.drug c_age cons, eform("exp(b)") nocons > > // example 2: > xi: glm studytime i.drug c_age cons, /// > family(gaussian) link(log) nocons eform > xi: poisson studytime i.drug c_age cons, irr vce(robust) nocons > > // example 3: > stset studytime, failure(died) > xi: stcox i.drug c_age > *-------------------- end example ----------------------- > > Hope this helps, > Maarten > > --- roland andersson <rolandersson@gmail.com> wrote: > >> Thank you Maarten. >> >> I have read someone mentioning to use survival analysis to model >> hospital LOS but have not seen it in practice or found a reference >> describing the principle. After reading your comment I found this >> reference (Basu A et al, Health Economics 2004) which describes the >> use of OLS on log transformed data, GLM models with a log-link and >> Cox >> regression on LOS data. Do you have another reference? >> >> I understand that this was a more problematic task than I first >> realised. I am not sure that using Cox regression wilkl solve my >> problem as I would like to simply express the difference in LOS in >> days after adjustment for confounders. I do not think that >> Cox-regression will help me there. >> >> I will read your other references on the interpretation of log >> transformed regression output. >> >> Greetings >> >> Roland Andersson >> >> 2008/11/3 Maarten buis <maartenbuis@yahoo.co.uk>: >> > 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 <rolandersson@gmail.com> 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 >> >> * >> >> * 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/ >> >> >> > >> > >> > ----------------------------------------- >> > 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/ >> > > > ----------------------------------------- > 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:*Maarten buis <maartenbuis@yahoo.co.uk>

**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>

- Prev by Date:
**Re: st: counting the number of times a string appears in a string variable?** - Next by Date:
**st: Reshape limit** - Previous by thread:
**RE: st: Interpretation of regressionmodel of ln-transformed variable** - Next by thread:
**Re: st: Interpretation of regressionmodel of ln-transformed variable** - Index(es):

© Copyright 1996–2015 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |