This is a request for an extension of the training program in Environmental Statistics at the Harvard T. H. Chan School of Public Health. The program prepares pre-doctoral and postdoctoral fellows for research in the application of biostatistics, statistical genetics and genomics, and data science to the environmental health sciences. The program will be administered through the Department of Biostatistics, with active participation by faculty members from the Department of Environmental Health and Department of Epidemiology, also located at the Harvard T. H. Chan School of Public Health. Trainees will receive high-quality instruction in basic biostatistical theory and methods, such as probability, statistical inference, computing and data analysis. The program will also provide training in specialized topics of particular relevance for environmental applications, such as longitudinal and multivariate data analysis, Bayesian methods, causal inference as well as statistical genetics, environmental genomics and other high-dimensional data techniques. The program also provides training in statistical methods relevant to environmental exposure assessment such as measurement error models, spatio-temporal methods, and data fusion methods for integrating exposure and health data from disparate temporal and spatial scales. Training will also be provided through substantive course work in environmental health, a regular seminar series called ?Environmental Statistics,? where faculty, students, and fellows present their own environmental health-related research, and annual symposia. An important focus of training will be the opportunity to collaborate with faculty members from all three participating departments on biostatistical research as it applies to environmental health. All trainees will participate in Harvard's program on scientific integrity in the conduct of research, formal coursework on grant writing strategies and methods to ensure reproducible science, formal, hands-on training in strategies for effective interdisciplinary collaboration, and regular workshops on effective writing strategies, public speaking, and overall career development. Since its inception in 1982, this training program has emphasized strong links to the environmental health sciences. In recent years, program trainers have placed particular importance on the recruitment of students from underrepresented minority groups. The focus on interdisciplinary training at the Harvard T. H. Chan School of Public Health, as well as its talented and diverse student body and faculty, makes it ideally suited for a training program in Environmental Statistics.

Public Health Relevance

The Training Program in Environmental Health Statistics at the Harvard T. H. Chan School of Public Health prepares pre-doctoral and postdoctoral fellows for research in the application of biostatistics, statistical genetics and genomics, and data science to the environmental health sciences. The Program combines first class training in biostatistical theory and methods, formal training in environmental health, practical experience in environmental health collaborations in interdisciplinary team science settings, and rigorous training in reproducible science. The Program provides trainees with the skills necessary for scientific leadership in interdisciplinary settings, focusing on skills for project management, grant writing, and other career development skills.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Institutional National Research Service Award (T32)
Project #
2T32ES007142-36
Application #
9490846
Study Section
Environmental Health Sciences Review Committee (EHS)
Program Officer
Shreffler, Carol A
Project Start
1983-07-01
Project End
2023-06-30
Budget Start
2018-08-01
Budget End
2019-06-30
Support Year
36
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
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