Single subject (SS) designs are popular in educational and psychological research. There exist several statistical techniques designed to analyze such data and to address the question of whether an intervention has the desired impact. Recently, researchers have suggested that generalized additive models (GAMs) might be useful for modeling nonlinear effects that are common with SS designs. This study sought to extend the use of GAM from SS to a research design in which individuals may be placed in separate groups and receive different interventions. Results of the simulation study found that using a mixed model form of GAM (GAMM) resulted in higher power for detecting actual effects in the population than was true for either GAM or a Bayesian GAM estimator. Thus, GAMMs are recommended for use with SS designs when interventions are expected to induce nonlinear relationships between time and the outcome variable and individuals receive different treatments.