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From |
"FEIVESON, ALAN H. (AL) (JSC-SD) (NASA)" <alan.h.feiveson@nasa.gov> |

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

Subject |
st: RE: Mixed effects or GEE? |

Date |
Tue, 12 Nov 2002 08:57:37 -0600 |

Mike - You should also consider the effect of time - i.e. as time goes on, the effect of the intervention might wane. Suppose you have a logistic regression model something like logit p(prescribe antibiotics)=f(time,intervention) + e In GEE, (with an equicorrelated assumption), you are only assuming equal correlation of e within dentists - no other distributional assumptions on e. With xtlogit, e is split into two random effects, between and within dentists, with each effect normally distributed and independent. So the equicorrelation arises because the between-dentist random effect is the same for all observations pertaining to that dentist. As in most statistical modelling problems, the more sophisticated model (xtlogit), if correct, gives greater power. However there is a risk of bias if the model is not correct. GEE makes fewer assumptions, so there is less risk of bias, but it would in general be less powerful. Also, in GEE you have the option of specifying other types of correlation within dentists - see manual. Hope this helps- Al Feiveson -----Original Message----- From: Mike Schmader [mailto:mikeschmader@yahoo.com] Sent: Tuesday, November 12, 2002 8:17 AM To: statalist@hsphsun2.harvard.edu Subject: st: Mixed effects or GEE? Dear listers, I am confused about the differences between random effects mixed effects and GEE models. I am looking at prescribing patterens of dentists. My outcome variable will be % of prescriptions written for antibiotics at baseline (t=0), 3 months (t=1), 6 months (t=2) and 12 months (t=3) post initiation of intervention mailings (educational material). Dentists were randomly assigned to intervention groups: group 1- "no intervention"; group 2- "low level of intervention"; group 3 -"high level of intervention". I will be analyzing this data with panel techniques but I am not sure whether I should use GEE, random effects, or mixed effects models. I will be using dentist ID as my clustering variable and indicator variables for level of intervention. Can anyone point me in the right direction as to the main differences in these techniques as I have not used these methods before? Thank you in advance. Mike __________________________________________________ Do you Yahoo!? U2 on LAUNCH - Exclusive greatest hits videos http://launch.yahoo.com/u2 * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: Mixed effects or GEE?***From:*Mike Schmader <mikeschmader@yahoo.com>

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