The Quantitative Sciences Unit (QSU) is a home for the practice of team science and for the development of the science behind team science. The QSU is a collaborative unit of 30 faculty and staff with diverse data science expertise in the Biomedical Informatics Research (BMIR) Division in the Department of Medicine (DOM). Faculty's research is steeped in team science principles, and staff are trained in the practice of both data and team science.
The mission of Stanford's Quantitative Sciences Unit is to improve public health and impact healthcare challenges by leading in the highest quality research of data science practices and creating a source of scholarship for leadership in and education to the interdisciplinary and translational research communities of Stanford and beyond.
The vision of Stanford's Quantitative Sciences Unit is two-part: 1. to establish a world-class academic data science leader that has a strong impact on public health while establishing Stanford as a universal leader in data science advancement; 2. to educate and train the next generation of scientists in the practice of data science.
More about QSU
QSU faculty build their labs on the shared infrastructure of the QSU. QSU members are data scientists with expertise including data inference, evidence synthesis, computational biology, missing data, prediction modeling, statistical computing, database creation, and software development. The QSU leads and collaborates on over 100 large-scale scientific projects that includes serving as a data coordinating center for large multi-center randomized clinical trials.
Our specific goals are to:
- Design studies that optimize the ease of interpreting results
- Analyze data using modern statistical techniques while applying best practice in rigor and reproducibility
- Develop new or adapt old methods for optimal analysis
- Securely house and track data in a HIPAA-compliant and IRB-compliant manner
- Create user-friendly publicly available software for recommended methods
- Interpret results
- Disseminate findings
- Mentor clinical investigators in research methods
Stanford has a rich history of doing high quality science. We are excited to be part of that process.
Stanford Data Science Resources:
The QSU leverages other data science resources within the Stanford School of Medicine. To learn about all these data science resources and to initiate a consultation, please visit the Stanford Data Science Resources web portal.
COVID DSMB Registry
As part of a larger effort to increase efficiencies and streamline infrastructure for clinical trials, the Stanford Quantitative Sciences Unit (QSU) has established a registry of experts who are interested in serving on (as a member or chair) and/or supporting (as an independent statistician) one or more data and safety monitoring boards (DSMBs) for trials studying interventions related to COVID-19. This registry will be available to researchers who are convening a DSMB. It is intended to be a tool to expedite the process and to fulfill the unique DSMB needs for COVID-related trials. The registry may potentially serve future trial needs as well.
We would like to encourage you to join this registry. Please note that participation in the COVID DSMB Registry in no way commits you to serve on a specific DSMB, although it will inform coordinators of your potential availability and experience.
If you are interested please enter your name and minimal information regarding your expertise click here.
Thank you in advance for considering participating in this important public health initiative.
Access to this tool is coming soon and will also be found on the Society of Clinical Trials COVID Research Resources Hub (https://www.sctweb.org/covid.cfm)!
We strongly believe diversity and inclusiveness is critical for doing good science. The QSU therefore strives to create an inclusive and diverse community where all are welcome and embraced.
Summer Han, PhD, QSU Faculty and Associate Professor of Neusurgery and Medicine's collaborative work with the CISNET Lung Group has been published in the Annals of Internal Medicine. This work demonstrates that risk model-based lung cancer screening is more cost-effective than the national lung cancer screening guidelines recommended by the U.S. Preventive Services Task Force (USPSTF). Please see the media coverage by MedPage featuring this finding.
Maya Mathur, PhD, QSU Faculty member and Assistant Professor of Pedatrics, was selected as the 2022 Society for Epidemiologic Research (SER) Brian MacMahon Early Career Epidemiologist Award recipient. This award acknowledges early career epidemiologists who have already made significant contributions to their field.
Zihuai He, PhD, QSU Faculty member and Assistant Professor of Neurology, published new feature selection methods in Nature Communications and The American Journal of Human Genetics to identify causual genetic variation of Alzheimer's disease.
Maya Mathur, PhD, QSU Faculty member and Assistant Professor of Pediatrics, is a recipient of the 2022 McCormick and Gabilan Faculty Awards. She was selected as one of six fellows to receive a 2022 McCormick and Gabilan Faculty Award, which was established to support the advancement of women in medicine and medical research.
Manisha Desai, PhD, QSU Director and Professor of Medicine and of Biomedical Data Science, assembled a community of researchers from the Schools of Medicine and Engineering to find the most effective methodologies for measuring children’s physical activity and sleep. Read more.
Toumazis I, Cao P, de Nijs K, Bastani M, Munshi V, Hemmati M, Ten Haaf K, Jeon J, Tammemägi M, Gazelle GS, Feuer EJ, Kong CY, Meza R, de Koning HJ, Plevritis SK, Han SS. (2023)
Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis.
Le Guen Y, Raulin AC, Logue MW, Sherva R, Belloy ME, Eger SJ, Chen A, Kennedy G, Kuchenbecker L, O'Leary JP, Zhang R, Merritt VC, Panizzon MS, Hauger RL, Gaziano JM, Bu G, Thornton TA, Farrer LA, Napolioni V, He Z, Greicius MD.(2023)
Association of African Ancestry-Specific APOE Missense Variant R145C With Risk of Alzheimer Disease
Mathur MB, Mathur VS. (2023)
Primary care physicians' perceptions of the effects of being overweight on all-cause mortality
Dahlen A, Charu V. (2023)
Analysis of Sampling Bias in Large Health Care Claims Databases
THE QSU IS HIRING!
QSU RESEARCH METHODS SEMINARS
3180 Porter Drive, Palo Alto, CA 94304
Room: B107 & B306
4-5pm first Tuesday of the month (unless otherwise stated)
Tuesday, April 4, 2023
Speaker: Will Stahl-Timmins, PhD
Talk Title: Visualizing health-related information at the BMJ (British Medical Journal)