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From |
"Loncar, Dejan" <LoncarD@unaids.org> |

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

Subject |
RE: st: Correction for bias in regression estimates after log transformation |

Date |
Wed, 17 Dec 2008 11:25:11 +0100 |

Many Thanks Marteen Dejan -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maarten buis Sent: 17 December 2008 10:49 To: statalist@hsphsun2.harvard.edu Subject: Re: st: Correction for bias in regression estimates after log transformation --- "Loncar, Dejan" <LoncarD@unaids.org> wrote: > I have transformed the variables using log function before > regression. > > Do you know by any chance which function in Stata or some ado file > can perform antilog transformation after regression with correction > for bias in regression estimates? Bias means nothing else than that your estimates don't mean what you think they mean. So there are two ways of addressing bias: Either you change interpretation of the results so that the interpretation corresponds to the estimate, or you change your estimate so that it measures what you think it does. Another consequence of this is that there is no such thing as a biased estimate perse: you always need to specify what the estimate is a biased estimate of. Trivially all estimates are biased estimates of most concepts (e.g. the annual tea consumption of Burundi is a biased estimate of the number of ants per square inch in Amsterdam), and at the same time all estimates are unbiased estimates of the thing that they measure (but the thing they measure may not be of interest). The distinction between changing the interpretation and changing the estimate is nicely illustrated by looking at a log transformed dependent variable. If you fist transform the dependent variable and than perform a regular regression you can interpret the exponentiated coefficients as ratios of geometric means, but not as ratios of arithmatic means. You can get estimates in terms of ratios of arithmatic means when you use -glm- on the untransformed dependent variable with -link(log)- option. So if you are interested in the effect on the geometric mean, then -glm- will provide you with biased estimates. You can solve this either by changing your interpretation of the results to the effect in terms of the arithmatic mean or by estimating your model with -regress-. I have discussed a detailed example of this issue here: http://www.stata.com/statalist/archive/2008-11/msg00137.html Also see: Roger Newson (2003) Stata tip 1: The eform() option of regress. The Stata Journal 3(4): 445. http://stata-journal.com/article.html?article=st0054 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/

**References**:**st: Correction for bias in regression estimates after log transformation***From:*"Loncar, Dejan" <LoncarD@unaids.org>

**Re: st: Correction for bias in regression estimates after log transformation***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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