# Center for Suicide Research and Prevention - Methods Core

> **NIH NIH P50** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $1,014,253

## Abstract

The Methods Core (MC) will facilitate efficient and robust transdisciplinary research for the proposed Center for
Suicide Prevention and Research (CSRP) through apposite application and evaluation of leading-edge
statistical approaches for integrative predictive analysis of complex real-world and clinical trial data. The
overriding goal of the MC is to support application and evaluation of sound statistical principles and
approaches to enhance and enable novel implementation strategies for improved suicide prevention efforts.
The MC team includes a distinguished team of biostatisticians and informaticians with expertise in clinical
trials, statistical and machine learning methods for high-dimensional real-world data, and a clinical informatics
team with expertise in development and implementation of Applications to be incorporated at the bedside. The
team members have an extensive history of collaboration with investigators constituting the proposed CSRP
and are extremely well-positioned to deliver (i) data collection, data synthesis and app development; (ii)
support the development and conduct of CSRP research studies; (iii) develop and validate novel clinical
prediction algorithms; and (iv) dissemination to the research community.

## Key facts

- **NIH application ID:** 10901839
- **Project number:** 5P50MH129699-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Andrea S Foulkes
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,014,253
- **Award type:** 5
- **Project period:** 2023-08-08 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901839

## Citation

> US National Institutes of Health, RePORTER application 10901839, Center for Suicide Research and Prevention - Methods Core (5P50MH129699-02). Retrieved via AI Analytics 2026-07-27 from https://api.ai-analytics.org/grant/nih/10901839. Licensed CC0.

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