Time-varying effect modeling to address new questions in behavioral research: Examples in marijuana use.

Time-varying effect modeling (TVEM), a statistical approach that enables researchers to estimate dynamic associations between variables across time, holds enormous potential to advance behavioral research. TVEM can address innovative questions about processes that unfold across different levels of time. We present a conceptual introduction to the approach and demonstrate 4 innovative ways to approach time in TVEM to advance research on the etiology of marijuana use. First, we examine changes in associations across historical time to understand how the link between marijuana use attitudes and marijuana use behavior has shifted from 1976 to present; gender differences in the relevance of attitudes diminished over time and were no longer significant after 2004. Second, we examine age-varying associations between heavy episodic drinking and marijuana use across developmental time and demonstrate that this dynamic association is substantially stronger during ages 14 to 16 compared with later ages. Third, we explore the complex association between age of onset of marijuana use and adult marijuana use to identify precise age ranges during which the onset of use is most risky, and demonstrate how this complex association is more salient for males. Finally, we examine changes in marijuana use as a function of time relative to the birth of first child and show how this transition is more crucial for females. All empirical examples in this methodological demonstration rely on existing data from cross-sectional or panel studies. We conclude with thoughts on future directions for the application and further development of TVEM in behavioral research. (PsycINFO Database Record