ABSTRACT
Many longitudinal health outcomes differ in severity and change over time. Describing outcome trajectories and associated clinical characteristics of individuals may help in developing a more personalized medicine approach. Growth mixture modeling (GMM) is a type of mixture modeling for investigating multiple, unobserved subgroups using longitudinal data. GMM is flexible, allowing investigation of antecedents and consequences of change. This chapter provides an overview and some examples of how GMM can be applied to answer some common types of research questions encountered by health researchers.
