This is a request for an extension of the training program in Environmental Health Statistics at the Harvard School of Public Health (HSPH). The program prepares pre-doctoral and postdoctoral fellows for research in the application of biostatistics, statistical genetics and genomics to environmental health. Administered through the Department of Biostatistics, the program features active participation by faculty members from the HSPH Departments of Environmental Health and Department of Epidemiology. Trainees will receive high-quality instruction in basic biostatistical models, such as probability, statistical inference, computing, and data analysis. The program also provides training in specialized topics of particular relevance for environmental applications, such as longitudinal and multivariate data analysis, causal inference, and missing data techniques, as well as statistical genetics, environmental genomics and other high-dimensional data techniques. In addition, the program provides training in statistical methods relevant to environmental exposure assessment such as measurement error models, spatial statistics, and data fusion methods for integrating exposure and health data from disparate temporal and spatial scales. Training includes applied course work in environmental health, a regular Environmental Statistics Seminar Series in which 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 integrityin the conduct of research, research workshops focusing on grantsmanship skills and project management, and formal, hands-on training in strategies for effective interdisciplinary collaboration. Since its inception in 1982, this training program has emphasized strong links to the environmental 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 School of Public Health, its rich research resources, and its talented and diverse student body and faculty makes it an ideal setting in which to provide a training program in environmental statistics. Public Health Relevance: The Training Program in Environmental Health Statistics at the Harvard School of Public Health prepares pre- doctoral and postdoctoral fellows for research in the application of biostatistics, statistical genetics and genomics to environmental health. The program combines first class training in biostatistical theory and methods, formal training in environmental health, and practical, hands-on experience with environmental health collaborations in interdisciplinary settings. The program provides trainees with skills necessary for scientific leadership in interdisciplinary settings, focusing on skills for project management, grant writing, and other career development skills.

Public Health Relevance

The Training Program in Environmental Health Statistics at the Harvard School of Public Health prepares pre- doctoral and postdoctoral fellows for research in the application of biostatistics, statistical genetics and genomics to environmental health. The program combines first class training in biostatistical theory and methods, formal training in environmental health, and practical, hands-on experience with environmental health collaborations in interdisciplinary settings. The program provides trainees with 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 #
2T32ES007142-31
Application #
8475169
Study Section
Environmental Health Sciences Review Committee (EHS)
Program Officer
Shreffler, Carol K
Project Start
1983-07-01
Project End
2018-06-30
Budget Start
2013-07-01
Budget End
2014-06-30
Support Year
31
Fiscal Year
2013
Total Cost
$417,912
Indirect Cost
$22,660
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Tabb, Loni Philip; Tchetgen, Eric J Tchetgen; Wellenius, Greg A et al. (2016) Marginalized zero-altered models for longitudinal count data. Stat Biosci 8:181-203
Barnett, Ian; Onnela, Jukka-Pekka (2016) Change Point Detection in Correlation Networks. Sci Rep 6:18893
Krier, Joel; Barfield, Richard; Green, Robert C et al. (2016) Reclassification of genetic-based risk predictions as GWAS data accumulate. Genome Med 8:20
Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent (2016) Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures. Biostatistics 17:377-89
Meyer, Mark J; Coull, Brent A; Versace, Francesco et al. (2015) Bayesian function-on-function regression for multilevel functional data. Biometrics 71:563-74
Smoot, E; Haneuse, S (2015) On the analysis of hybrid designs that combine group- and individual-level data. Biometrics 71:227-36
Staples, Patrick C; Ogburn, Elizabeth L; Onnela, Jukka-Pekka (2015) Incorporating Contact Network Structure in Cluster Randomized Trials. Sci Rep 5:17581
Alexeeff, Stacey E; Schwartz, Joel; Kloog, Itai et al. (2015) Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data. J Expo Sci Environ Epidemiol 25:138-44
Bobb, Jennifer F; Valeri, Linda; Claus Henn, Birgit et al. (2015) Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics 16:493-508
Bobb, Jennifer F; Peng, Roger D; Bell, Michelle L et al. (2014) Heat-related mortality and adaptation to heat in the United States. Environ Health Perspect 122:811-6

Showing the most recent 10 out of 87 publications