This is a request for an extension of the training program in Environmental Health Statistics at the Harvard School of Public Health. The program prepares pre-doctoral and postdoctoral fellows for research in the application of biostatistics to environmental health. 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 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 analysis, missing data techniques, statistical genetics and high-dimensional data analysis, and statistical methods relevant to environmental exposure assessment such as measurement error models and spatial statistics. Training will also be provided through applied course work in environmental health and a regular seminar series called "Environmental Statistics Seminar Series", where faculty, students, and fellows present their own environmental health-related research. An important focus of training will be the opportunity to collaborate with faculty members 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 as well as professional development workshops focusing on strategies for success in academic settings. 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, as well as its talented and diverse student body and faculty, makes it ideally'suited for a training program in Environmental Statistics. BACKGROUND This is a competing continuation application for a Training Program in Environmental Statistics at the Harvard School of Public Health. The program was started in 1982 and has been continuously supported. The emphasis of program is to train biostatisticians and other quantitative scientists in the theory and practice of statistical science with application to environmental health research. The application is justified based on the increasing need to analyze complex data in environmental health that resulted from the recent advances in data collection technology, and significant interaction of the training program with other funded research projects in environmental health. New over the past five-year funding cycle are two features. In 2003, the Department of Biostatistics entered into an arrangement with the Statistics Department and the Graduate School of Arts and Sciences (GSAS) at Harvard to offer the Ph.D. instead of the S.D. offered by the School of Public Health. This impacts the Training Program by making available the recruiting efforts from the GSAS for qualified Ph.D. candidates;students now qualify for additional scholarships available only to Harvard Ph.D. students (important for under-represented minorities);and this change improves the availability of research mentoring from GSAS faculty in Statistics. Additionally, in 2003 when Dr. Ryan was on sabbatical, Dr. Coull took over as Director of the program. Since her return, Dr. Ryan has been named the co-Director. In 2005, two new faculty members were hired with research interests in environmental statistics. The Training Program is currently administered using an "Executive Committee approach" (Dr. Coull as Director, Drs. Ryan and Lin serve as co-Directors, with Dr. Paciorek "involved in all administrative decisions").

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Environmental Health Sciences Review Committee (EHS)
Program Officer
Shreffler, Carol K
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard University
Biostatistics & Other Math Sci
Schools of Public Health
United States
Zip Code
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