Logistic Regression Using SAS®: Theory and Application

8.7. Mixed Logit Models

There is another approach to dichotomous clustered data that is likely to become increasingly attractive in the next few years. In my judgment, however, it’s not quite ready for prime time. In this approach, clustering is treated as a random effect in a mixed model—so named because it includes both fixed and random effects. Mixed models are quite similar to the GEE method discussed earlier but have two potential advantages. First, much more complex models are possible, with multiple levels of clustering, overlapping clusters, and random coefficients. Second, estimation of mixed models can correct for heterogeneity shrinkage discussed in Section 3.11. In other words, mixed models are subject specific rather than population ...