Mixed Effects Logistic Regression
- Models both fixed (population-level) and random (group- or subject-level) effects in one regression.
- Useful when data are hierarchical or nested, for example with repeated observations within subjects or groups.
- Lets you account for individual- and group-level variation (e.g., subject, therapist, teacher) while estimating effects of predictors (e.g., dosage, time, age, gender).
Definition
Section titled “Definition”Mixed-effects logistic regression is a type of regression analysis that allows for the examination of both fixed and random effects within a single model. This type of analysis is useful when studying data that has a hierarchical or nested structure, such as when multiple observations are made within each subject or group.
Explanation
Section titled “Explanation”Mixed-effects logistic regression combines fixed effects (predictors assumed to have the same effect across the population) with random effects (terms that capture variation across subjects, groups, or other clusters). It is applied when observations are not independent because they belong to higher-level units (for example, repeated measures within a person or students within a class). Including random effects enables the model to represent unique patterns and trends at both the individual and group levels while estimating the influence of fixed predictors.
Examples
Section titled “Examples”Clinical trial (depression medication)
Section titled “Clinical trial (depression medication)”Each subject receives a dosage of the medication and is assessed for improvement in their symptoms over time. The fixed effects in this model might include the dosage of the medication and the time of assessment, while the random effects could include the individual subject and the therapist who is conducting the assessment.
Classroom academic achievement
Section titled “Classroom academic achievement”Each student is observed over the course of a school year and their academic performance is measured at various points throughout the year. The fixed effects in this model might include the student’s age and gender, while the random effects could include the teacher and the class in which the student is enrolled.
Use cases
Section titled “Use cases”- Analysis of hierarchical or nested data where observations are grouped (for example, repeated measures within subjects or students within classes).
- Situations requiring simultaneous estimation of population-level predictors and group- or subject-level variability.
Related terms
Section titled “Related terms”- Fixed effect
- Random effect
- Hierarchical data
- Nested structure
- Logistic regression