Abstract

Longitudinal study designs are implemented when one or more responses are measured repeatedly on the same individual or experimental unit. These designs often seek to characterize time trajectories for cohorts and individuals within cohorts. Three broad categories of longitudinal designs include repeated measures or growth curve designs, where multiple responses for each individual are observed over time or space under the same intervention or other conditions; crossover designs, where individual responses are measured over sequences of interventions so that individuals each “cross over” from one intervention to another; and follow-up studies, where individuals in a cohort are followed until the time that they either have an “event” or have not had an event but have no further follow-up information. Longitudinal designs may be either randomized where individuals are randomly assigned into different groups or observational where individuals from different well-defined groups are observed over time. In this chapter, I briefly discuss the nature of each of the three designs above and more deeply explore visualization and some analysis techniques for repeated measures design studies via examples of the analyses of two datasets. I conclude with discussion of recent topics of interest in the modeling of longitudinal data including models for intensive longitudinal data, latent class models, and joint modeling of survival and repeated measures data.