Methodological innovations to advance substance use disorder research: Proceedings of a NIDA workshop on target trial emulation and translational testing of digital health tools.

内容摘要

Substance use disorder (SUD) is a complex chronic condition requiring a multi-disciplinary approach to both research and treatment. Randomized controlled trials (RCTs) are gold standard methodologies for inferring causal relationships between an intervention and treatment outcomes but often face challenges in generalizability, scalability and real-world implementation. Target trial emulation (TTE) is a powerful methodological framework that uses observational or real-world data sources to emulate the methodology of these gold standard target trials to complement the learning from RCTs and enhance translation to real world evidence. An additional methodological innovation is the translational testing of clinical- and community-based digital health systems to provide new insights into SUD in the real world and provide scalable access to therapeutic resources. To explore these methodological innovations in SUD research, the National Institute on Drug Abuse Center for the Clinical Trials Network convened a variety of experts for a virtual workshop titled "Target Trial Emulation in Observational Research and Translational Testing of Advanced Digital Health Tools for Substance Use Disorder Prevention and Treatment." This article summarizes the discourse of the workshop, focused on three thematic areas: TTE using real-world healthcare data, SUD evidence from nationwide data sources that may be useful in TTE analyses, and translational testing of clinical- and community-based digital health systems. The workshop also highlighted various exemplars of digital health systems that demonstrate success in translational research addressing SUDs, key methodological and translational challenges, importance of rigorous study design, robust data linkages and expanding use of common data elements, and the integration of digital health tools to enhance causal inference and clinical impact. Future research directions are outlined to refine these approaches, address barriers, and maximize the utility of real-world data in shaping effective SUD prevention and treatment strategies.