The Precision Health Research Pillar in the Division of Biostatistics is a vibrant hub of collaborative research. Faculty, PhD students, research scientists, and postdoctoral fellows engage actively in precision medicine research, meeting approximately once per month to discuss their ongoing work. The group focuses on developing innovative multidimensional data reduction techniques to model complex datasets—such as high-dimensional, functional, or imaging data—to better characterize heterogeneity in biological phenomena in response to treatments or exposures. Another central aim is advancing quantitative nosology, with a strong emphasis on neuropsychiatry as a motivating field.

The group organizes a Precision Health Interest Group (PHIG) seminar series to foster discussion and exchange of ideas. Recent research highlights include developing a single index model for longitudinal outcomes to optimize individualized treatment decision rules, Bayesian approaches for analyzing brain functional connectivity, and functional additive models for treatment optimization. Other notable projects involve K-tensor clustering for functional connectivity matrices and Bayesian scalar-on-network regression methods. Together, these efforts reflect the group’s commitment to pushing the boundaries of precision health research.

Lead Faculty

Hyung G. Park, PhD
Assistant Professor

Nicholas Illenberger, PhD
Assistant Professor

Thaddeus Tarpey, PhD
Professor

Faculty Members

Hayley Belli, PhD
Assistant Professor

Ting-Fang Lee
Research Assistant Professor

Huilin Li, PhD
Professor

Sharon Meropol, MD, PhD
Research Assistant Professor

Hakhamanesh Mostafavi, PhD
Assistant Professor

Michele Santacatterina, PhD
Assistant Professor

Our Methodology Research

• H. Park, E. Petkova, T. Tarpey, and R. T. Ogden. A single-index model with a surface link for optimizing individualized dose rules. Journal of Computational and Graphical Statistics, 2022, 31:563-562, PMCID: PMC9306450.
• H. Park, E. Petkova, T. Tarpey and R. T. Ogden, RT. Functional additive models for optimizing individualized treatment rules. Biometrics, 2023, 79, 113-126, PMCID: PMC9043034. 
• N. Illenberger, A. J. Spieker, and N. Mitra. Identifying optimally cost-effective treatment regimes with a Q-learning approach. Journal of the Royal Statistical Society: Series C, 2023, 72(2), 434-449.

Our Collaborative Research

• E. Petkova, H. Park, H., A. Ciarleglio, RT Ogden, and Tarpey, T. (2020), “Optimising treatment decision rules through generated effect modifiers: a precision medicine tutorial, British Journal of Psychiatry Open, 6(1), e2, PMCID: PMC7001471.
• H. Park, T. Tarpey, M. Liu, et al. Development and validation of a treatment benefit index to identify hospitalized patients with COVID-19 who may benefit from convalescent plasma. JAMA Network Open, 2022, 5(1):e2147375. PMCID: PMC8790670.

Contact Us

For more information about the Precision Health Research Pillar, please contact Thaddeus Tarpey, PhD, at Thaddeus.Tarpey@NYULangone.org.