Federal Project Grant Award Summary

The National Institute of Mental Health (NIMH), under its Mental Health Research Grants program (CFDA 93.242), awarded $759,816 to the University of California, Berkeley, effective May 1, 2026, through January 31, 2031. This Project Grant supports the development and application of targeted machine learning estimators (TMPLEs) designed to address critical methodological gaps in cluster randomized trials (CRTs) evaluating HIV prevention implementation strategies. The research directly responds to the persistent global challenge of 1.3 million new HIV infections annually despite expanded access to high-efficacy biomedical prevention products. The primary products deliverable through this funding include novel statistical and computational methods to minimize bias from missing data in CRTs—a common problem when intervention strategies are deployed at the group level and outcomes are only measured among reached individuals—and data-driven approaches to evaluate effect modification across diverse populations and contexts.

The project combines formal causal modeling, statistical theory, and machine learning methodologies to generate methodological advances that will be disseminated through peer-reviewed publications, computational tools, and technical guidance to the implementation science research community. By addressing limitations in existing analytical approaches for CRTs, this research aims to enhance the rigor and interpretability of future HIV prevention trials, ultimately supporting more effective and equitable deployment of biomedical prevention strategies across health systems and clinical settings.