This is a competitive renewal of a highly successful short term training program in Biostatistics at the Harvard School of Public Health. This program targets undergraduate students who are members of groups who are underrepresented in the field of Public Health, including, primarily minority groups, and who are pursuing an undergraduate degree in mathematics, computer science, physics, engineering or another quantitative field. Support is requested for six 4-week internship slots. Among the activities supported by the program are introductory coursework in biostatistics and statistical computing, research seminars, small group research projects, career planning workshops, a practice GRE examination and a variety of social activities. Group projects will be designed each year in collaboration with faculty from the Departments of Biostatistics, Epidemiology, Society, Human Development and Health, and Environmental Health at the Harvard School of Public Health. The project will include data analysis, report writing, and oral presentation. All students who participate in the program will be strongly encouraged to go on to graduate school. While the emphasis will be on the fields of biostatistics and epidemiology, students will learn about a variety of research career options, including various aspects of environmental science. Promising students will be encouraged to apply to the investigators' School and will be given strong consideration for support on this training grant in Environmental Biostatistics (5-T32-ES07142) or on the other training grants in cancer and AIDS, for example. The progress over the past five years has been excellent. Sixty- students have participated in the program since 1994. Of the 43 students in the program who have finished their undergraduate degrees as of this time, 26 (62%) have gone on to pursue graduate training in statistics or biostatistics. Ten have entered the Biostatistics program and Harvard. The program is already having a measurable positive impact on recruitment and retention in the Department.
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|Izu, Alane; Cohen, Ted; Mitnick, Carole et al. (2011) Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains. Stat Med 30:2708-20|
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