Joint modeling of longitudinal data in multiple behavioral change

Abstract

Multiple behavioral change is an exciting and evolving research area, albeit one that presents analytic challenges to investigators. This manuscript considers the problem of modeling jointly trajectories for two or more possibly non-normally distributed dependent variables, such as marijuana smoking and risky sexual activity, collected longitudinally. Of particular scientific interest is applying such modeling to elucidate the nature of the interaction, if any, between an intervention and personal characteristics, such as sensation seeking and impulsivity. The authors describe three analytic approaches: generalized linear mixed modeling, group-based trajectory modeling, and latent growth curve modeling. In particular, the authors identify identify the strengths and weaknesses of these analytic approaches and assess their impact (or lack thereof) on the psychological and behavioral science literature. The authors also compare what investigators have been doing analytically versus what they might want to be doing in the future and discuss the implications for basic and translational research.