# Design, Data and Biostatistics Core

> **NIH NIH P30** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $201,614

## Abstract

Abstract
The Design, Data and Biostatistics Core (DDBC), previously known as the Methodology, Data Management
and Analysis Core (MDMAC), is an integral part of the UM Pepper Center. The primary goal of DDBC is to
provide methodological, data management, and analytical support to OAlC affiliated investigators, that address
the focus of the OAlC - inflammation, metabolism, predictors and interventions for function of elderly people -
as well as aging research in general. In collaboration with other OAIC Cores, DDBC will improve the quality of
OAIC research studies, help foster development of junior researchers, will nurture forming interdisciplinary
research groups, and ultimately enhance quality of research on late-life processes. DDBC faculty will address
the following four aims: 1. Advise and assist OAlC investigators in methodological design, and analytical tasks
in conducting research projects; 2. Training and mentoring for OAlC investigators; 3. Facilitate the access of
OAlC investigators to secondary survey and administrative research data sets used for observational
population research; 4. Undertake a limited number of internal efforts/projects to identify in existing literature or
develop novel methodological approaches, and implement and disseminate them as software tools.

## Key facts

- **NIH application ID:** 10448482
- **Project number:** 5P30AG024824-18
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Andrzej T Galecki
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $201,614
- **Award type:** 5
- **Project period:** 2004-09-30 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10448482

## Citation

> US National Institutes of Health, RePORTER application 10448482, Design, Data and Biostatistics Core (5P30AG024824-18). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10448482. Licensed CC0.

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