Model-building with Complex, High-dimensional Exposures
Creators: name: University Of California Office Of The President
Publisher: name: University of California Office of the President
publisherIdentifier: https://ror.org/00dmfq477
publisherIdentifierScheme: ROR
Year: 2024

description: Introduction: Cancer epidemiologists are beginning to generate data sets that are much larger and many times more complex than what has been available to them in the past. Analyzing data sets of ever increasing size and complexity presents daunting obstacles. Unfortunately, the tools typically used by epidemiologists to analyze their data are not sufficient. At the same time, computer-intensive, statistical methods are being developed to solve complex problems in other scientific areas, like the Human Genome Project. The overall goal of this proposal is extend these computer-intensive methods to the complex cancer data sets. As a concrete example, we will use data from a large, ongoing study to address an important public health topic: understanding the relationship between agricultural pesticide use and the occurrence of breast cancer.
 
 
 
 
 
 
 
 Question: Can statistical data mining methods being developed to explore and discover associations and relationships in large, complex data sets be profitably applied to determining which, if any, of the thousands of pesticide compounds being used in California agriculture pose a risk of breast cancer?
 
 General methodology: Researchers will focus on two unique California resources. The first, the California Teachers Study, is an ongoing research effort begun in 1995 and involving over 130,000 active and retired California teachers. The second is California’s Pesticide Use Reporting System. This system has tracked every commercial application of over 1000 different agricultural pesticides throughout the state of California since 1990. The database contains information about the what, when, where, who, how, and how much, of every agricultural pesticide application in California. Integrating these data sets provides a unique opportunity to evaluate the relationship between pesticide exposures and breast cancer. The researchers will then use expert knowledge of pesticides to apply statistical data mining methods, and to determine whether these methods can do better than simpler, more traditional methods.
 
 
 
 
 
 
 
 Innovative elements: To date, computer-intensive methods have mainly been applied to biological problems and used on data with a large number of very simple variables. For instance, the Human Genome Project considered each of the over 30,000 different genes independently; only recently have scientists begun to explore more complex relationships among the genes. This is one of the first studies to explore the use of data mining methods with the more complex types of data that cancer epidemiologists produce. In addition, project researchers will take into account the complex relationships among pesticides and use expert knowledge the health effects of pesticides to organize and guide this exploration.
 
 
 
 
 
 
 
 Community involvement: The California Teachers Study has actively involved members of the community from its inception. The CTS External Advisory Committee includes members from teachers’ unions, from the State Teachers Retirement System, and from community-based breast cancer advocacy organizations. Feedback from these groups, as well as from members of the cohort itself (as part of an email feedback system in place for the CTS), has been influential in determining ongoing research priorities for the study.