JSM 2005 - Toronto

Abstract #303836

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 157
Type: Luncheons
Date/Time: Monday, August 8, 2005 : 12:30 PM to 1:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #303836
Title: Hypothesis Versus Data-driven Modeling
Author(s): Paola Sebastiani*+
Companies: Boston University
Address: Department of Biostatistics, Boston, MA, 02118, United States
Keywords: association discovery ; Bayesian networks
Abstract:

A common approach to the analysis of associations between risk factors and an outcome is "hypothesis driven," in which investigators have a hypothesis about risk factors on an outcome and possible confounders. Although legitimate in many applications, this approach leaves the information available in large observational studies often unexploited. On the other hand, data mining and knowledge discovery methods developed in the machine learning community favor a "data-driven" approach to discover unsuspected associations from the data. Both approaches have pros and cons, and an ideal procedure to association discovery should combine hypothesis and data-driven methods for a coherent mining of the data. Bayesian networks, also known as directed graphical models, provide an example of a flexible modeling tool that has been used successfully to model complex systems from data in different domains, from gene expression, to survey data, to genetic epidemiology. Examples will be used to show the usefulness of Bayesian networks and their current limitations.

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Revised March 2005