Defining cancer subtypes with distinctive etiologic profiles: An application to the epidemiology of melanoma Journal Article
| Authors: | Mauguen, A.; Zabor, E. C.; Thomas, N. E.; Berwick, M.; Seshan, V. E.; Begg, C. B. |
| Article Title: | Defining cancer subtypes with distinctive etiologic profiles: An application to the epidemiology of melanoma |
| Abstract: | We showcase a novel analytic strategy to identify subtypes of cancer that possess distinctive causal factors, that is, subtypes that are “etiologically” distinct. The method involves the integrated analysis of two types of study design: an incident series of cases with double primary cancers with detailed information on tumor characteristics that can be used to define the subtypes; a case-series of incident cases with information on known risk factors that can be used to investigate the specific risk factors that distinguish the subtypes. The methods are applied to a rich melanoma dataset with detailed information on pathologic tumor factors, and comprehensive information on known genetic and environmental risk factors for melanoma. Identification of the optimal subtyping solution is accomplished using a novel clustering analysis that seeks to maximize a measure that characterizes the distinctiveness of the distributions of risk factors across the subtypes and that is a function of the correlations of tumor factors in the case-specific tumor pairs. This analysis is challenged by the presence of extensive missing data. If successful, studies of this nature offer the opportunity for efficient study design to identify unknown risk factors whose effects are concentrated in defined subtypes. Supplementary materials for this article are available online. © 2017 American Statistical Association. |
| Keywords: | logistic regression; k-means clustering; multiple imputation; etiologic heterogeneity; case–control study |
| Journal Title: | Journal of the American Statistical Association |
| Volume: | 112 |
| Issue: | 517 |
| ISSN: | 0162-1459 |
| Publisher: | American Statistical Association |
| Date Published: | 2017-03-01 |
| Start Page: | 54 |
| End Page: | 63 |
| Language: | English |
| DOI: | 10.1080/01621459.2016.1191499 |
| PROVIDER: | scopus |
| PMCID: | PMC5460661 |
| PUBMED: | 28603323 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 June 2017 -- Source: Scopus |
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