BBB researchers develop broadly applicable biostatistical and computational methods and algorithms for designing and analyzing studies conducted in the Division of Intramural Population Health Research (DIPHR) and the Division of Intramural Research. Some of these methods and algorithms are also applicable to research conducted at NIH and in the extramural scientific community. In addition to developing novel methods, BBB members collaborate with DIPHR researchers on almost all aspects of studies, including developing valid and testable scientific hypotheses, designing efficient and powerful studies, analyzing and interpreting the resulting data, and preparing manuscripts for publication.
The Branch's mission is to:
- Develop original biostatistical and bioinformatics research relevant for the research mission of the Division and institute.
- Engage in collaborative research with other Division, institute, and extramural investigators working in research areas relevant for the Division and institute.
- Provide service to the Division, institute, NIH, HHS, and other government agencies via consultation, collaboration, and assistance to advance the scientific discipline of biostatistics and the goals of the institute.
- Recruit and mentor highly qualified students and trainees at various stages of their careers to position them for professional careers in biostatistical and bioinformatics research.
BBB invites candidates to apply for tenure-track investigator positions (PDF 196 KB) to establish independent research programs in modern areas of computational statistics and data science.
Congratulations to DIPHR scientists who recently received awards from the American Statistical Association:
- Rajeshwari (Raji) Sundaram received the 2020 Jeanne E. Griffith Mentoring Award .
- Katherine Laughton Grantz and Raji Sundaram received the 2020 Outstanding Statistical Application Award , along with colleagues from the Johns Hopkins School of Public Health, for their paper Joint Modeling of Competing Risks Data and Current Status Data: An Application to Spontaneous Labour Study, published in the Journal of the Royal Statistical Society, Series C (Applied Statistics) in 2019.
Lin, H., & Peddada, S. D. (2020). Analysis of compositions of microbiomes with bias correction. Nature Communications, 11(1), 3514. PMID: 32665548.