# Data Science Core

> **NIH NIH P30** · JOHNS HOPKINS UNIVERSITY · 2020 · $159,421

## Abstract

PROJECT SUMMARY
During the last decade the Johns Hopkins Division of Rheumatology has prioritized the
development of longitudinal patient cohorts across seven disease-specific Centers of
Excellence, with comprehensive clinical, diagnostic, lab, patient-reported, and imaging data, and
biospecimens to facilitate discovery. These datasets have now matured to allow greater insight
into how multiple factors influence the development and progression of rheumatic diseases and
variation in treatment responses. Statistical methods to evaluate and interpret longitudinal data
have advanced substantially in recent years, especially for identifying relevant subgroups of
patients and predicting trajectories of disease progression and responses to therapy. Many of
these methods were developed by members of our team and are only just beginning to be
applied to medicine. The Specific Aims of the Data Science Core are to: 1) Provide data
analytical support throughout the research process that will enable investigators to generate,
manage, analyze, and interpret data using modern statistical methods; 2) Develop and apply
Bayesian hierarchical models (BHMs) to longitudinal cohorts bringing together with diverse
sources of data to identify patient subsets predictive of different trajectories of outcomes and
responses to treatments; and 3) Apply modern analytic approaches to observational and
experimental studies that rigorously address heterogeneity among individuals in their responses
to treatments. The Data Science Core will be led by Dr. Scott Zeger who pioneered advances in
longitudinal data analysis methods, and leads the transformative Hopkins inHealth Initiative. The
Data Science Core will optimize its impact and maximize productivity and efficiency by
embedding biomedical data science faculty within each of our Centers of Excellence. We are
confident that by working together, our clinician scientists and biomedical data scientists
(biostatisticians) can accelerate the pace of research discoveries by analyzing our rich datasets
carefully collected over time with new advances in statistical modeling to identify multiple factors
that influence the onset and course of rheumatic diseases and better predict individual
responses to different treatments that in turn can help guide evidence-based clinical practice.

## Key facts

- **NIH application ID:** 9996494
- **Project number:** 5P30AR070254-05
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** SCOTT L. ZEGER
- **Activity code:** P30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $159,421
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9996494

## Citation

> US National Institutes of Health, RePORTER application 9996494, Data Science Core (5P30AR070254-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9996494. Licensed CC0.

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