If you have longitudinal data (which I assume you do so that's why you're using a mixed model) you probably need to think more carefully about the analysis and how you are handling the longitudinal nature of the data. For example, do you need to define a 'baseline' for everyone and re-code the data to have 1 observation per person (with clearly defined covariates that are pre-treatment and outcomes that are post-treatment), or do you need to use fancier methods like marginal structural models, which can handle time-varying confounders and treatments? It seems like it might not be a simple solution, but a lot depends on the details of your data and analysis. Liz On 2/1/12 2:16 PM, "Francois Maurice" <maurice.francois(a)ymail.com> wrote: Hi, I'm trying to implement the example at page 18 from the MatchIt manual. The example is about a way to estimate the ATT. I can't implement the example because I'm using model="ls.mixed" so my dataframe has to be in a long format. In concrete terms, when I extract my dataframe with match.data() to restructure it in long format, I can't use the option data=match.data(mydata, "control") within Zelig. Is there a workaround ? I tried to use matchit() with the long format dataframe, but it gives wrong mathching since each case has multiple observations in that format. Thanks, François Maurice