The Biostatistics & Bioinformatics Shared Resource (BBSR) provides Lombardi research investigators with a centralized resource with expertise in the biostatistical and bioinformatics aspects of clinical, basic science, and population science research projects. Statistical issues are considered at all levels of investigation from the design to the conduct of experiments, maintenance of data quality, and the analysis and interpretation of results. Bioinformatics pertains mainly to data organization, annotation, and database mining for biological relevance. Specifically, the primary objectives of the BBSR are:
- To collaborate with Lombardi investigators on the biostatistics/bioinformatics aspects of basic science, clinical, and population science research projects, especially those likely to lead to research grant support,
- To participate effectively in the clinical trials program by providing biostatistics/bioinformatics input to the planning of all Lombardi clinical trials, by active membership on the Clinical Research Committee and providing biostatistical reviews of proposed protocols, and by the monitoring of all Lombardi trials through the Data and Safety Monitoring Committee,
- To educate Lombardi investigators, staff and students in biostatistics/bioinformatics methodology for the planning, conduct, analysis and interpretation of cancer research studies,
- To perform research in biostatistics/bioinformatics methodology on problems arising in collaborations with investigators on cancer research projects, and
- To coordinate with GUMC Biomedical Informatics Centers and to implement a common user interface for all Lombardi shared databases.
Learn more about our Educational Resources.
Ming Tan, PhD, Professor and Chair
Telephone: (202) 687-2511
Subha Madhavan, PhD
Telephone: (202) 687-5068
Fax: (202) 687-2581
In all publications that include data derived or methods used from the Biostatistics & Bioinformatics Shared Resource, please acknowledge our resource. The Biostatistics & Bioinformatics Shared Resource is partially supported by NIH/NCI grant P30 CA051008 and GHUCCTS grant UL1 TR000101.