What generalized linear mixed model do you use for 1) binary 2) count data?

Study for the ACVPM Epidemiology and Biostatistics Exam. Prepare with flashcards and multiple choice questions, with hints and explanations for each. Be exam-ready!

Generalized linear mixed models handle different outcome types by pairing a link function with a distribution for the data and adding random effects to account for correlation within clusters or repeated measures. For a binary outcome, the natural approach is logistic regression with a logit link, and when data are clustered, you include a random effect to form a logistic mixed model. For count data, the natural approach is Poisson regression with a log link, and similarly you add a random effect to create a Poisson mixed model. Putting those together, the standard choice is a logistic GLMM for binary data and a Poisson GLMM for counts, both incorporating random effects to account for non-independence. (Note: if counts show overdispersion, a negative binomial GLMM with random effects can also be appropriate, but Poisson with random effects is the typical default.)