Research People and Projects

Official Title:

Promoting Rapid Uptake of Multilevel Latent Class Modeling via Best Practices: Investigating Heterogeneity in Daily Substance Use Patterns

The unprecedented amount of high-quality, intensive longitudinal data (ILD) on substance use and associated risk factors holds critical information that can help explain substance use etiology and inform the development of the next generation of interventions that are tailored to those who need it, when they need it. However, advanced methods for analyzing ILD—including multilevel latent class analysis (MLCA) and multilevel latent transition analysis (MLTA)—are needed to characterize the heterogeneity of patterns of use that unfold in daily life and to identify novel day-level intervention targets. These methods enable researchers to discover clinically relevant substance use behavior patterns and day-to- day transitions, dynamic risk factors embedded in daily life that are associated with problematic use patterns, and person-level characteristics that indicate for whom these risk factors are most salient. Guided by a socio-ecological framework, this project evaluates, applies, and disseminates MLCA and MLTA to analyze ILD. Results will guide prevention scientists and clinicians in designing interventions targeting the most salient risk factors for specific individuals at the best time for intervention.