Online Program Home
  My Program

Abstract Details

Activity Number: 407 - Data Science Applications in Epidemiology
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #322867 View Presentation
Title: Dimension Reduction in the Study of Etiologic Heterogeneity
Author(s): Emily Zabor* and Colin Begg
Companies: Memorial Sloan Kettering Cancer Center and Memorial Sloan Kettering Cancer Center
Keywords: cancer epidemiology ; clustering ; model selection ; etiologic heterogeneity
Abstract:

As molecular and genomic profiling of tumors has become increasingly common, the focus of cancer epidemiologic research has shifted away from the study of risk factors for disease as a single entity, and toward the identification of subtypes of disease. A number of statistical methods have been proposed for the study of risk factor differences across disease subtypes, a concept known as etiologic heterogeneity. While available statistical methods perform well when the number of characteristics that combine to form disease subtypes is not too large, there is a need for approaches that focus on dimension reduction in this context. One approach is to reduce up front the number of individual tumor characteristics available for study through variable selection whereas an alternative is to use clustering techniques to reduce dimension through identification of disease subtypes based on all available characteristics. We compare and contrast these approaches to dimension reduction, and seek to determine whether they can be profitably combined to identify the most etiologically distinct subtypes of disease.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association