Biostatistics for precision oncology and clinical trials

I'm an associate professor in Biostatistics at UNC Gillings and Lineberger, where I develop statistical methods and software for cancer research. My work bridges methodological innovation with clinical application—from adaptive trial designs that integrate real-time biomarker data to machine learning tools for tumor classification and missing data problems. As co-director of the LCCC Biostatistics Shared Resource and biostatistics cores for two NCI SPOREs (breast and pancreatic cancer), I collaborate with oncologists and trialists to translate statistical methods into clinical decision support tools.

Focus areas Adaptive platforms • Tumor subtyping • Missing data ML
Clinical translation CLIA-certified diagnostics • ARPA-H ADAPT Co-PI • Dual SPORE cores

Research Overview

Purpose

We build statistical methods and software in partnership with oncology teams to support biomarker discovery, clinical trials, and translational research.

Collaboration with UNC Lineberger researchers shapes our problems and keeps tools aligned with clinical needs, from trial design to data analysis and software delivery.

Machine Learning & Biostatistics

Missing data deep learning (dlGLM, NIMIWAE) Penalized mixed models AI trial navigation tools
Explore methodology →

Precision Medicine & Genomics

PurIST genomic classifier for tumor subtyping Multi-omic integration for treatment stratification RNA-seq and epigenomic analysis pipelines
View translational projects →

Software

epigraHMM for multi-condition epigenomics glmmPen + dlGLM Docker stacks for shared HPC NIMIWAE imputation methods for registries
Browse software packages →

Collaboration-Driven Methodology

Our statistical methods emerge from embedded clinical partnerships rather than abstract theory. Working directly with UNC Lineberger oncologists, we identify unmet analytical needs in real trials and translate them into rigorous, reproducible computational solutions.

This approach ensures methodological innovations address practical challenges in precision oncology—from single-sample tumor subtyping to adaptive platform designs that integrate serial biomarker data. Close collaboration with cancer researchers allows us to develop tools that are both statistically principled and clinically actionable.

Clinical Impact & Translation

Active Funding

Current roles

  • Co-PI and statistical member for the ARPA-H ADAPT platform (metastatic breast cancer)
  • Co-director of NCI breast and pancreatic cancer SPORE biostatistics cores
  • PI on DOD-funded LLM clinical trial navigation tool
View full funding portfolio →

Recognition & Leadership

Recent Honors

Awards & Recognition

  • 2025 — Gillings Research Excellence Award
  • 2024 — James E. Grizzle Distinguished Alumnus Award
  • 2023 — Teaching Innovation Award, UNC Gillings
  • 2021 — Delta Omega Faculty Award, Gillings School of Global Public Health
  • 2017 — IBM and R.J. Reynolds Junior Faculty Development Award, UNC-CH
  • 2013 — Barry H. Margolin Dissertation Award for best doctoral dissertation

National Leadership

Collaborative Roles

  • Breast SPORE Core B Co-Director (P50-CA058223, 2024–2029): Biostatistics support for 4 translational projects, TBCRC trial correlatives, and ctDNA resistance monitoring
  • Pancreatic SPORE Core C Co-Director (P50-CA257911, 2022–2027): Integrated quantitative science for 5 SPORE projects spanning organoid models, immunotherapy, and stromal reprogramming
  • Lineberger LCCC Biostatistics Shared Resource Associate Director (P30-CA016086): Statistical leadership for 40+ cancer center investigators annually, trial design consultation, grant development, regulatory analytics
  • Nature Medicine Statistical Advisory Panel (2024-)
  • Associate Editor, Annals of Applied Statistics (2023-)
  • V Foundation Scientific Advisory Board (2023-)
  • TBCRC Statistical Working Group (2017-)

Recent Invited Talks

Recent Seminar Topics

Recent seminar topics include adaptive oncology statistics, biomarker-driven trial platforms, and AI-assisted decision tools. Talks are typically 45–60 minutes and can be tailored for biostatistics, oncology, or data science audiences. I am happy to meet with students and participate in chalk talks or lab meetings.

Invite Dr. Rashid to speak
May 2025 ARPA-H ADAPT Analytics Summit Washington, DC & virtual

Bayesian adaptive methods for metastatic breast cancer platforms

Presented reinforcement learning, platform-level borrowing, and LLM tools for the ADAPT network.

October 2024 James E. Grizzle Distinguished Alumnus Lecture, UNC Gillings Chapel Hill, NC

Replicability, semi-supervised learning and generative AI: recent statistical work in cancer biostatistics

Grizzle Award Lecture covering recent methodological developments in replicable genomic prediction, semi-supervised learning for cancer subtyping, and generative AI applications.

June 2024 STATGEN 2024 Conference (Invited Talk) Pittsburgh, PA

Joint Nonnegative Matrix Factorization and Survival Modeling to Select Clinically-relevant Gene Signatures

Presented joint NMF-survival modeling methods for selecting clinically-relevant gene signatures in cancer genomics.

Find information most relevant to you

For Prospective Students

Interested in PhD training in adaptive trial design, AI/ML, or cancer genomics?

View Training Program →

For Funders & Reviewers

Evaluating research portfolio, productivity, or collaborative impact?

Download CV →

Research Focus

Methodological innovations in adaptive trials, tumor subtyping, and missing data.

Explore research →

Software & Tools

Open-source R packages and CLIA-certified diagnostic tools.

Browse software →

Funding & Support

ARPA-H, NCI, DOD funding for adaptive platforms and precision oncology.

View grants →

News

Site

Aug 15, 2024

The updated Rashid lab website has gone live!

Latest Updates

Selected Publications

  1. Biostat
    Differential Transcript Usage Analysis Incorporating Quantification Uncertainty Via Compositional Measurement Error Regression Modeling
    Amber Marie Young*, S. Van Buren*, and N.U. Rashid
    Biostatistics, 2024
  2. JCGS
    Deeply Learned Generalized Linear Models with Missing Data
    David K Lim, Naim U Rashid, Junier B Oliva, and Joseph G Ibrahim
    Journal of Computational and Graphical Statistics, 2024
  3. Biometr
    Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects
    Hillary M Heiling*, N.U. Rashid, Quefeng Li, Xianlu L Peng, Jen Jen Yeh, and Joseph G Ibrahim
    Biometrics, 2024
  4. JCO
    DNA mutational profiling in patients with colorectal cancer treated with standard of care reveals differences in outcome and racial distribution of mutations
    Federico Innocenti, Wancen Mu, Xueping Qu, Fang-Shu Ou, Omar Kabbarah, Charles David Blanke, Alan P Venook, Heinz-Josef Lenz, and N.U. Rashid
    Journal of Clinical Oncology, 2024
  5. JASA
    Modeling Between-Study Heterogeneity for Improved Reproducibility in Gene Signature Selection and Clinical Prediction
    N.U. Rashid, Quefeng Li, Jen Jen Yeh, and Joseph G Ibrahim
    Journal of the American Statistical Association, 2020
  6. JASA
    High-Dimensional Precision Medicine From Patient-Derived Xenografts
    N.U. Rashid, Daniel J Luckett, Jingxiang Chen, Michael T Lawson, Longshaokan Wang, Yunshu Zhang, Eric B Laber, Yufeng Liu, Jen Jen Yeh, Donglin Zeng, and  others
    Journal of the American Statistical Association, 2020
  7. CCR
    Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer
    N.U. Rashid, Xianlu L Peng, Chong Jin, Richard A Moffitt, Keith E Volmar, Brian A Belt, Roheena Z Panni, Timothy M Nywening, Silvia G Herrera, Kristin J Moore, and  others
    Clinical Cancer Research, 2020
  8. Nat Gen
    Virtual microdissection identifies distinct tumor-and stroma-specific subtypes of pancreatic ductal adenocarcinoma
    Richard A Moffitt, Raoud Marayati, Elizabeth L Flate, Keith E Volmar, S Gabriela Herrera Loeza, Katherine A Hoadley, N.U. Rashid, Lindsay A Williams, Samuel C Eaton, Alexander H Chung, and  others
    Nature genetics, 2015