Mixed effects models and generalized linear models (GLMs) are combined in generalized linear mixed models (GLMMs). When dealing with data that is not regularly distributed and has correlations and hierarchical structures, they are helpful. GLMMs work well with hierarchical data because they take into account both fixed and random effects. They employ link functions to connect variables and maximum likelihood estimation of parameters.

GLMMs can detect regional clusters, temporal trends, and demographic discrepancies in the context of police fatal shootings. They aid in determining risk variables and evaluating how policy changes may affect the number of fatal shootings.