Generalised linear mixed models: a binary outcome with grouped or clustered data. The mixed-model counterpart of GLM.
Note
These pages are a recognition index — organised by the shape of the analysis, not by which MCPower feature they show off. If your outcome is continuous, you want mixed models; if your data is not grouped, see GLM.
Examples
- Cluster-randomised binary trial (random intercept per cluster)
infection ~ treatment + (1|hospital)— one between-cluster treatment effect on a yes/no outcome, clusters randomised whole. - Longitudinal binary outcome over time (random intercept per subject)
symptom_present ~ month + treatment + (1|patient)— a yes/no outcome tracked over time within subjects across two arms. - Difference-in-differences on a binary outcome (group x time GLMM)
employed ~ policy_group * period + (1|individual)— the group-by-time interaction (DiD) on a clustered binary outcome. - Logistic GLMM with a continuous predictor and random slope
species_present ~ temperature + (1 + temperature|site)— a continuous predictor whose slope varies across groups.