# Combining data sources to identify effect moderation for personalized mental health treatment

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $425,757

## Abstract

Project Summary
Determining “what works for whom” is a key goal in prevention and treatment across a variety of
areas, including mental health. By understanding which individuals benefit most from which
treatments we have the possibility of directing scarce resources to those who will most benefit,
and of reducing the “churn” of individuals attempting multiple treatments before finding the one
that works for them. Identifying effect moderators—factors that relate to the size of treatment
effects--is crucial for delivery of treatment and prevention interventions, but doing so is
incredibly difficult using standard study designs. Randomized trials, the gold standard for
estimating average effects, are typically under-powered to detect moderation. Large-scale non-
experimental studies may provide another way to examine effect moderation, but can suffer
from confounding. New methods are needed to best harness the data available to learn how to
personalize mental health treatments. This work will synthesize, extend, and apply methods for
identifying effect moderators when multiple studies are available, with a particular focus on the
complexities in mental health research. The methods will apply broadly and will be illustrated in
an example estimating the effects of medication treatment for schizophrenia, using data from 11
randomized controlled trials and non-experimental data from the Duke University Health System
electronic health record. The work will: 1) Extend moderation methods for scenarios with
multiple randomized experiments, 2) Develop methods for using data from combined datasets
with both experimental and non-experimental designs to identify effect moderation, and 3)
Disseminate the methods to mental health researchers. By developing methods to take full
advantage of both experimental and non-experimental data this work has the potential to move
towards personalized mental health, thus improving how we prevent and treat mental health
challenges in the population.

## Key facts

- **NIH application ID:** 10854922
- **Project number:** 5R01MH126856-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Elizabeth A. Stuart
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $425,757
- **Award type:** 5
- **Project period:** 2021-08-19 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10854922

## Citation

> US National Institutes of Health, RePORTER application 10854922, Combining data sources to identify effect moderation for personalized mental health treatment (5R01MH126856-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10854922. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*