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From |
Clyde Schechter <clyde.schechter@einstein.yu.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: baseline adjustment in mixed models |

Date |
Sat, 14 Nov 2009 13:01:49 -0800 |

I hesitate to disagree with Martin Buis, but perhaps I have interpreted your question differently. I suppose you have an outcome y observed on each participant at each time, a variable group (coded 0 for control, 1 for intervention), a participant identifier variable, id, and a variable, time, which might be actual times of observation, or just a sequence 0 through N, whatever). If your time variable is not coded as baseline = 0, it will make life simpler if you transform it so that is the case. In the example below I will assume that you plan to model y as a linear function of time, because that is the simplest from a coding perspective. If you need a more complicated representation with dummies for different times, or a spline, etc., you can modify accordingly. Again, life is simplest if the baseline measurement corresponds to time = 0 (or the omitted time category if dummies are used) in your coding. If you want to test whether the intervention has modified the response trajectory over time, the key is to test for the group X time interaction. gen groupXtime = group * time xtmixed y group time groupXtime || id: gives you a random intercept model. The coefficient of group represents the mean difference of y between intervention and control groups when time = 0. That is, this model does incorporate, and in a useful sense, "adjusts for" the baseline difference between the groups. The coefficient of groupXtime represents the difference in the slopes of the y-time lines between groups. Now, you might want to make this more sophisticated if you anticipate that individuals, within each group, might have different individual y-time slopes, in which case a random slopes model might be better: xtmixed y group time groupXtime || id: time Again, baseline differences in y are satisfactorily accounted for in this model, and are even directly estimated by the coefficient of group. Again, the key inference is based on the coefficient of the interaction term. With either of these models you should get good, serviceable estimates of the group difference in average y-time slope. Note that this approach is different from adjusting for baseline response by excluding the time 0 observations from the model and including time 0 response as a covariate in an ANCOVA like analysis. Hope this helps. Clyde Schechter Associate Professor of Family & Social Medicine Albert Einstein College of Medicine, Bronx, NY, USA * * 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**:**st: RE: baseline adjustment in mixed models***From:*"Visintainer PhD, Paul" <Paul.Visintainer@baystatehealth.org>

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