Statistical Innovations in Collaborative Prevention Research: Adventures in Matching Methods to Research Questions

The roundtable, featuring experts in quantitative methods and public health, explored how innovation in statistical design can elevate prevention research—when paired with thoughtful application and ethical foresight. Dr. Bayless highlighted Omni’s approach, which emphasizes foundational quantitative principles like reproducibility and transparency while remaining agile enough to integrate emerging techniques. Innovation, she argued, isn’t just about using the newest tool; it’s about choosing the best-fit method to answer the question at hand. At Omni, this means sometimes adapting existing tools in novel ways and always focusing on how findings will be understood and used by partners.

Panelists also discussed the risks of deploying under-tested methods in community-engaged research, especially when those risks are borne disproportionately by the people whose lives are being studied. Dr. Bayless emphasized Omni’s commitment to balancing innovation with responsibility—using feasibility assessments, pivot strategies, and clear communication to mitigate harm. Ultimately, the roundtable reinforced that statistical tools should never drive the question—they should serve it, and only then can they meaningfully contribute to positive social change.