# Targeted Machine Learning to evaluate and optimize HIV prevention strategies in cluster randomized trials

> **NIH MH R01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2026 · $759,816

## Abstract

PROJECT SUMMARY/ABSTRACT
Globally, there were 1.3 million new HIV infections in 2023, despite expanded access to biomedical HIV
prevention products with high efficacy. Implementation strategies are needed to expand the reach of HIV risk
screening and to facilitate the use of biomedical prevention among persons with risk. These implementation
strategies are often delivered at the group-level or induce changes at the group-level (e.g., health clinics or health
systems). Cluster randomized trials (CRTs) are integral to evaluating and optimizing strategies deployed at the
group-level. CRTs provide an exciting opportunity to evaluate strategies aiming to both improve reach into the
target population and health outcomes among persons reached. However, these CRTs create a complex missing
data problem: the strategy improves outcomes directly and indirectly; yet, outcomes are only measured among
persons reached. While machine learning can facilitate adjustment for missing data in simpler CRT settings, new
methods are needed to minimize bias arising from this common CRT setting. CRTs also provide an exciting
opportunity for intervention optimization by evaluating for whom and in what context the strategy works best.
However, existing methods to evaluate effect heterogeneity in CRTs are prone to false conclusions (i.e., Type-I
and Type-II errors). While machine learning can facilitate data-driven evaluation of effect modification in
individually randomized trials, CRTs present distinct challenges due to their small effective sample sizes. In this
proposal, we will address these crucial gaps in the analysis of CRTs. To do so, we will develop, apply, and
disseminate new Targeted Machine Learning Estimators (TMLEs) to minimize bias due to missing data and to
facilitate data-driven evaluation of effect modification. TMLE combines formal causal modeling, statistical theory,
and machine learning to improve the accuracy, precision, and relevance of our findings. This proposal has the

## Key facts

- **NIH application ID:** 11328733
- **Project number:** 1R01MH140685-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Laura B Balzer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** MH
- **Fiscal year:** 2026
- **Award amount:** $759,816
- **Award type:** 1
- **Project period:** 2026-05-01T00:00:00 → 2031-01-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11328733

## Citation

> US National Institutes of Health, RePORTER application 11328733, Targeted Machine Learning to evaluate and optimize HIV prevention strategies in cluster randomized trials (1R01MH140685-01A1). Retrieved via AI Analytics 2026-07-12 from https://api.ai-analytics.org/grant/nih/11328733. Licensed CC0.

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