Nested designs, also known as hierarchical designs, are a type of experimental design where the levels of one factor (subgroups) exist only within one level of another factor. Essentially, the groups are “nested” within each other.

Mixed effects models (also known as multilevel models or hierarchical models) are statistical models that incorporate both fixed effects and random effects.

• Fixed Effects: These are the main effects of interest in a study. They are assumed to be the same across different groups or settings. In a biomedical context, this could be the specific treatments or interventions being tested.

• Random Effects: These account for natural variability in the data that arises from random sampling or inherent differences across subjects or clusters. They allow for the intercepts and/or slopes to vary across groups.

In this app you can simulate a longitudinal study in which two groups (treatment and control) are compared. In each group, you have different subjects and for each subject, you have repeated measurements at each time. In that case, time is considered a factor variable (for instance different control visits in a clinical trial

In this app you can define a longitudinal study and select the treatment effect at each data point. The data are fitted by lmer using splines. In that case, time is considered a continuous variable.