Scientific data pertaining to risks from exposure to environmental agents are broad and diverse addressing issues ranging from expression of messenger RNA at the cellular level to mortality patterns in diverse populations. Much of the statistical research relating to these types of data has focused on methods for testing hypotheses in single data sets. However, as we begin to understand the mechanisms through which environmental agents cause toxicity, the linkages between various types of data gain credibility and use. The challenge facing statisticians active in this area is to develop methods and procedures that address a large fraction of the data simultaneously so that decisions made concerning environmental hazards are based upon objective analyses of as much of the data as possible. This talk will focus on the use of parametric models to test hypotheses concerning environmental hazards and to estimate risks; not from single, isolated analyses but from complex evaluations linking these diverse data sets.
Learning Objectives: After this talk, the attendee will be able to: 1. Describe mechanism-based linkages between different types of environmental toxicology data. 2. Identify parametric modeling approaches to combining information from multiple diverse data sets to assess the toxicity of an environmental hazard.
Keywords: Environmental Health Hazards, Biostatistics
Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
Disclosure not received
Relationship: Not Received.