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.

One example of a situation where mixed-effects logistic regression might be used is in a clinical trial examining the effectiveness of a new medication for treating depression. In this study, 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.

Another example of mixed-effects logistic regression could be in a study examining the factors that predict academic achievement in a classroom setting. In this study, 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.

Overall, mixed-effects logistic regression allows for the examination of both fixed and random effects within a single model, providing a more comprehensive analysis of the data and allowing for the identification of unique patterns and trends within the data. This type of analysis is particularly useful in studies with hierarchical or nested data structures, as it allows for the examination of both individual-level and group-level effects.