The recent explosion in data ensures a continued need for biostatisticians, statistical geneticists, and data scientists with training in the environmental health sciences. Rigorous training in biostatistics, statistical genetics/genomics, and data science for environmental health sciences requires not only offerings in statistical theory and methods, but also in specialized research areas such as measurement error methods, methods for assessing gene-environment interactions and other genomic applications, spatial-temporal methods, and other methods for high-dimensional biologic and exposure data. All of these topics are current areas of methodological development and training in the Department. Further, training leaders in interdisciplinary environmental research requires training in substantive environmental fields. The program at Harvard University is one of a small number of centers in the US that provides the kind of high-quality training that is needed on all fronts contained in this training program: statistical science, data science, genetics and genomics, and environmental health.
Training is also 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 is the opportunity to collaborate with faculty members from all three participating departments on biostatistical research as it applies to environmental health. All trainees 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 indisciplinary collaboration, and regular workshops on effective writing strategies, public speaking, and overall career development.
Stipend and tuition support for this training program is funded through a National Institutes of Health grant (T32 ES007142).