I am confused by statements at a UCLA webpage about mixed effects logistic regression. They show a table of fixed effects coefficients from fitting such a model and the first paragraph belows seems to interpret the coefficients exactly like a normal logistic regression. But then when they talk about odds ratios, they say you have to interpret them conditional on the random effects. What would make the interpretation of the log-odds different than their exponentiated values?
- Wouldn't either require "holding everything else constant"?
- What is the proper way to interpret fixed effect coefficients from this model? I was always under the impression nothing changed from the "normal" logistic regression because the random effects have expectation zero. So you interpreted log-odds and odds ratios exactly the same with or without random effects - only the SE changed.
The estimates can be interpreted essentially as always. For example, for IL6, a one unit increase in IL6 is associated with a .053 unit decrease in the expected log odds of remission. Similarly, people who are married or living as married are expected to have .26 higher log odds of being in remission than people who are single.
Many people prefer to interpret odds ratios. However, these take on a more nuanced meaning when there are mixed effects. In regular logistic regression, the odds ratios the expected odds ratio holding all the other predictors fixed. This makes sense as we are often interested in statistically adjusting for other effects, such as age, to get the “pure” effect of being married or whatever the primary predictor of interest is. The same is true with mixed effects logistic models, with the addition that holding everything else fixed includes holding the random effect fixed. that is, the odds ratio here is the conditional odds ratio for someone holding age and IL6 constant as well as for someone with either the same doctor, or doctors with identical random effects