# Data Science Core

> **NIH NIH P30** · JOHNS HOPKINS UNIVERSITY · 2022 · $175,637

## Abstract

PROJECT SUMMARY
The broad goal of the Data Science Core (DSC) is to advance and apply modern data science
methods to support the practice of precision medicine in rheumatology. We define precision
medicine as discovering mechanistically anchored and clinically relevant disease subgroups for
which optimal strategies can be followed. The precision medicine statistical methods we
develop and apply will improve the clinical assessment of an individual's disease status,
trajectory, and likely benefits of competing interventions and thereby benefit people living with
rheumatic diseases. During the past funding period, a team led by P30 PI Antony Rosen and
DSC Director Scott Zeger, designed and implemented the JH Precision Medicine Analytics
Platform (PMAP), a cloud-based data system: (1) receives a nightly download of all JHM clinical
data and projects the data into a secure clinical cohort database; (2) provides an environment
for collaborative, modern statistical analyses; and (3) provides tools to visualize from within the
Electronic Medical Record those analytic results relevant to a particular patient's decisions.
PMAP has already been implemented in the Scleroderma and Myositis Center. During the
coming period, PMAP will become active in each rheumatology Center of Excellence so that
current clinical cohort databases will form the information foundation for the next phase of this
P30 program. This novel infrastructure makes it possible for the DSC to develop and apply
novel statistical tools that identify disease subgroups, for whom optimal treatments can be
evaluated. In this way, JHM Rheumatology will discover the key data science tools required by a
learning healthcare system.
The Specific Aims of the Data Science Core are to: (1) provide study design and analysis
support that enables investigators to generate, manage, analyze, and interpret complex data
using modern statistical and computing methods; (2) develop and apply multivariate hierarchical
models (MHMs) to longitudinal datasets to identify patient subsets predictive of disease
trajectories and major clinical events; and (3) apply modern statistical analytic approaches to
observational and experimental studies that rigorously estimate treatment efficacy and safely,
acknowledging the likely heterogeneity among individuals in their responses to treatments and
the potential biases inherent in using observational data to address causal questions.

## Key facts

- **NIH application ID:** 10487462
- **Project number:** 5P30AR070254-07
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** SCOTT L. ZEGER
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $175,637
- **Award type:** 5
- **Project period:** 2016-09-09 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10487462

## Citation

> US National Institutes of Health, RePORTER application 10487462, Data Science Core (5P30AR070254-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10487462. Licensed CC0.

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