Modeling Repeated Measurements with Random Effects

This chapter delves into the significance of considering repeated measurements as non-independent within individuals and the benefits of using random effects to incorporate variability. It discusses strategies for more accurate data representation and estimation of factors like treatment effects by accounting for patient-specific errors and exploring different covariance structures in SAS.