Discussion on modeling assumptions, software preferences, and resources for effective statistical modeling

This chapter emphasizes the significance of making rational assumptions about participant trajectories post-dropout and missing data in linear mixed models, delving into fixed versus random effects and techniques like multi-level modeling. It explores the preference for using SAS in modeling despite progress in other software like ARRA, showcasing the MMRM package and SAS functionalities for managing extensive datasets and estimation algorithms.