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Biostatistics & Informatics

Division members engage in statistical methods development and application, research design and statistical computing for health science research. Division faculty are critical leaders on research teams uniting faculty from the Department of Preventive Medicine as well as the Feinberg School of Medicine and the university as a whole.

Faculty interests encompass Bayesian methods, bioinformatics, causal inference, computational biology, clinical trials, diagnostic testing, longitudinal modeling, missing data modeling, observational data methods, semi-parametric models, spatial modeling, statistical genetics and survival analysis.

Our division’s premiere faculty and staff biostatisticians are the driving force of data analytic innovation and excellence at Northwestern’s Feinberg School of Medicine and beyond. Through methodology development, research team and core leadership, reproducible analytic applications and educational efforts, the Division of Biostatistics perpetuates the highest standards for health research.”

Denise M. Scholtens, PhD

Denise M. Scholtens, PhD

Centers & Cores

Biostatistics Collaboration Center

The BCC, supported by Northwestern University Clinical and Translational Sciences Institute, offers Northwestern investigators education and expert support in biostatistics, epidemiology, programming, and data management at every phase of their projects.

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Northwestern University Data Analysis and Coordinating Center (NUDACC)

NUDACC partners with multidisciplinary teams in collaborative leadership of multicenter clinical research. Capabilities encompass study design, data collection tool development, data monitoring and curation, regulatory reporting and statistical analysis.
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Quantitative Data Sciences Core

This core facility of the of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University offers collaborative support and expertise to scientists in biostatistics, bioinformatics and clinical informatics.
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What We Do

 Research

The mission of the Division of Biostatistics is to advance the science of biostatistics and to advance biomedical research through world-class biostatistical collaboration. Learn more about major research initiatives within our division and the groups/studies with whom we collaborate.

 Seminars

View our Events page for all upcoming seminars.

The Division of Biostatistics hosts two seminar series. The DPM-Biostatistics Seminar Series that focuses on methodological and theoretical topics is held on Mondays from 3-4pm. The Applied Statistics Seminar Series that focuses on applied analytic and programming techniques is held on Tuesdays from 12-1pm. All seminars are held in the Stamler Conference Room (680 N. Lake Shore Dr., Suite 1400, Chicago, IL 60611) unless noted otherwise. For more information, please click on the speaker's name to add the event to your calendar via Plan-It Purple or email sarah.hill1@northwestern.edu.

Faculty Spotlight

Bonner, Lauren Balmert
Dr. Bonner's research interests are primarily collaborative and include the application of biostatistics to a wide range of areas including HIV maternal child health, communication sciences and disorders, anesthesiology, and pediatric medicine.
Lee, Jungwha
Dr. Lee's research interests include statistical methods in epidemiology, longitudinal data analysis, and multilevel analysis. Her work on partial population attributable fraction has focused on estimating variance and computing confidence intervals to make inference on competing risk factors. She is currently interested in methodological issues relevant to the development and evaluation of risk prediction models for various study designs and data for model building, including cohort, nested case-control, case-control data combined with registry data, and kin-cohort data.
Muhammad, Lutfiyya N
Dr. Muhammad's research interests include collaborative research and statistical applications to biomedical research especially related to rheumatic diseases. Her methodological research is related to longitudinal data, linear mixed models, truncated data, and missing data.
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