The principal objectives of the training grant are to address the emergent needs cancer research for well-trained statisticians to integrate, analyze and interpret complex clinical datasets, as well as high throughput biologic datasets. This training program involves more than thirty accomplished biostatisticians, mathematicians, computer scientists and computational biologists, along with world-renowned experts in cancer treatment and research, with the overarching goal of providing the trainees with the essential tools needed in modern cancer research. The specific goals of the training program are to train students and postdoctoral fellows to be (1) quantitative scientists in cancer research, who are capable of using probability, statistics, computer science and mathematics to increase our knowledge and understanding of cancer; (2) strong team leaders/players and excellent communicators in a cancer research environment, who can effectively disseminate their research results and assume active roles in the design, analysis and interpretation of cancer genomic studies, cancer clinical trials and cancer prevention trials.

All students supported by this training grant are required to take a concentration in cancer-related courses. During the first and second summer periods in the program, students are required to participate in the research activities of the DFCI, working under the supervision of faculty mentors affiliated with this program. As students complete their coursework and select their dissertation advisors and research topics, most take up residence at the DFCI to undertake their research. All postdoctoral fellows are closely involved with the practice of statistics in cancer and are in residence at the DFCI. Both pre- and postdoctoral trainees are required to actively participate in the Harvard seminar series on quantitative issues in cancer research, which serves as a primary forum at Harvard to discuss current issues and challenges on this topic.

Stipend and tuition support for this training program is funded through a National Institutes of Health grant (T32 CA09337).