Biostatistics Branch
Meet Paul Albert & Ruth Pfeiffer in the Biostatistics Branch
Developing novel statistical methodology for epidemiology and genetics
Investigators in the Biostatistics Branch (BB) develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.
Research Mission
The mission of the branch is “to contribute to the understanding of cancer etiology and to improve public health through the development and application of quantitative methods.”
BB scientists strive to achieve their goals by:
- Collaborating on and selectively leading scientific studies responding to the scientific priorities of DCEG and NCI;
- Developing new statistical methodologies for epidemiologic, genomic and laboratory studies;
- Developing and distributing tools and analytic software for research use within and outside the DCEG community; and
- Providing unique biostatistics training within a scientifically rigorous interdisciplinary cancer research environment.
In DCEG, BB scientists have an ideal environment in which to solve important problems in population-based cancer research. By developing and using cutting-edge statistical methodologies, BB scientists can design and analyze data more effectively, taking full advantage of our rich data sources. BB scientists work as collaborators on all major initiatives within the Division, identifying key questions or problems that require new methods and solutions, both for study design and to make important inferences in DCEG studies. Learn more about BB research areas.
Fellowships
BB offers opportunities for postdoctoral research in statistical methods for epidemiologic and genetics research. Areas of interest include methods for descriptive epidemiology, risk prediction, screening, environmental epidemiology, dose-response assessment, high-dimensional and longitudinal biomarkers, and population-based inference using sampling methodology.
Other areas include methods for genomic studies including GWAS analysis, somatic mutation analysis, and integrative tumor analysis. Postdoctoral fellows also have an opportunity to collaborate on important epidemiologic studies, many of which include molecular and genetic components.
These opportunities allow recent Ph.D. recipients to build their methodological and collaborative research programs. Strong candidates from statistics and biostatistics doctoral programs who are eligible to work in the U.S. are encouraged to apply. Preference will be given to candidates interested in applied problems, and with superior communication skills.
Meet our current BB fellows and learn about BB research training opportunities.
Collaboration
Branch investigators are key participants in large, complex, interdisciplinary studies in collaboration with scientists throughout DCEG, across the NCI and NIH, and with investigators and public health officials at other government agencies and academic and research institutions in the U.S. and abroad.
Research Highlights
- Ancestry-adjusted Model to Facilitate Patient Engagement in Lung Cancer Prevention
- Zirpoli GR et al. Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women. Breast Cancer Res. 2024.
- Novel Method Improves Polygenic Risk Prediction for Diverse Ancestries
- Twelve Lung Adenocarcinoma Risk Variants Identified for East Asian Individuals
- Liu D et al. Ascertainment of incident cancer by US population-based cancer registries versus self-reports and death certificates in a nationwide cohort study, the US Radiologic Technologists Study. Am J Epidemiol. 2022.
- Egemen D, et al. Variation in Human Papillomavirus Vaccination Effectiveness in the US by Age at Vaccination. JAMA Network Open. 2022.
- Palmer JR, Zirpoli G, Bertrand KA, et al. A Validated Risk Prediction Model for Breast Cancer in US Black Women. J Clin Oncol. 2021. Read the NCI Cancer Currents blog post.
- Cheung LC, et al. Risk-Based Selection of Individuals for Oral Cancer Screening. J Clin Oncol. 2021.
- Katki HA, Bebu I. A simple framework to identify optimal cost-effective risk thresholds for a single screen: Comparison to decision curve analysis. Journal of the Royal Statistical Society (Statistics in Society, Series A). 2021.
- Zhang H, Deng L, Wheeler W, et al. Integrative analysis of multiple case-control studies. Biometrics. 2021.
- Haber G, Malinovsky Y, Albert PS. Is group testing ready for prime-time in disease identification? Statistics in Medicine. 2021.