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A conceptual and methodological framework for investigating etiologic heterogeneity
Author(s) -
Begg Colin B.,
Zabor Emily C.,
Bernstein Jonine L.,
Bernstein Leslie,
Press Michael F.,
Seshan Venkatraman E.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5902
Subject(s) - predictability , disease , conceptual framework , genetic heterogeneity , computational biology , computer science , data science , biology , medicine , pathology , genetics , gene , sociology , statistics , social science , mathematics , phenotype
Cancer has traditionally been studied using the disease site of origin as the organizing framework. However, recent advances in molecular genetics have begun to challenge this taxonomy, as detailed molecular profiling of tumors has led to discoveries of subsets of tumors that have profiles that possess distinct clinical and biological characteristics. This is increasingly leading to research that seeks to investigate whether these subtypes of tumors have distinct etiologies. However, research in this field has been opportunistic and anecdotal, typically involving the comparison of distributions of individual risk factors between tumors classified on the basis of candidate tumor characteristics. The purpose of this article is to place this area of investigation within a more general conceptual and analytic framework, with a view to providing more efficient and practical strategies for designing and analyzing epidemiologic studies to investigate etiologic heterogeneity. We propose a formal definition of etiologic heterogeneity and show how classifications of tumor subtypes with larger etiologic heterogeneities inevitably possess greater disease risk predictability overall. We outline analytic strategies for estimating the degree of etiologic heterogeneity among a set of subtypes and for choosing subtypes that optimize the heterogeneity, and we discuss technical challenges that require further methodologic research. We illustrate the ideas by using a pooled case‐control study of breast cancer classified by expression patterns of genes known to define distinct tumor subtypes. Copyright © 2013 John Wiley & Sons, Ltd.