Environmental Statistics Facility Core

Overview

The Environmental Statistics Core replaces what was formerly known as the "Biostatistics and Data Management Facility Core." The overall purpose of the Core will remain unchanged, namely to provide intellectual guidance for the statistical design and analysis of studies involving Harvard NIEHS Center investigators. The changed name reflects a broadening of our Specific Aims to reflect our expertise in quantitative methods for environmental risk assessment, Geographic Information System (GIS) and spatial statistics, as well as molecular biology and statistical genetics.

Specific aims for the Environmental Statistics Facilities Core are:

To provide Center investigators with state-of-the-art support in terms of statistical design and analysis for preparation of scientific articles, abstracts, presentations, and grant applications. Expertise will be available for both classical problems in statistical design and analysis, as well as more specialized problems in the areas of genomics and molecular biology (the subspecialty of Bioinformatics);
To establish a state-of-the-art GIS facility and to enhance the Center's expertise in spatial methods for health research;
To provide a focal point for Center investigators interested in quantitative risk assessment;
To promote interdisciplinary research involving the development of new statistical methods for the design and analysis of environmental health studies;
To disseminate knowledge of state-of-the-art statistical methodology through publication of more expository type papers in environmental health journals, as well as through Harvard's Environmental Statistics webpage;
To train students and postdoctoral fellows from the Department of Biostatistics in statistical methodology research related to environmental health, as well as in collaborative research related to environmental health;
To educate Center investigators on new statistical methods applicable to their research activities. Areas of special interest include repeated measures models, measurement error, computational biology, statistical genetics, and Bayesian methods; and
To provide guidance on data management and computational issues, as needed.