Statistical methods for studying disease subtype heterogeneity |
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| Authors: | Molin Wang Donna Spiegelman Aya Kuchiba Paul Lochhead Sehee Kim Andrew T. Chan Elizabeth M. Poole Rulla Tamimi Shelley S. Tworoger Edward Giovannucci Bernard Rosner Shuji Ogino |
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| Affiliation: | 1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.;2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.;3. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A.;4. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.;5. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, U.S.A.;6. Department of Biostatistics, National Cancer Center, Tokyo, Japan;7. Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, U.S.A.;8. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, U.S.A.;9. Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A.;10. Department of Medical Oncology, Dana‐Farber Cancer Institute, Boston, MA, U.S.A. |
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| Abstract: | A fundamental goal of epidemiologic research is to investigate the relationship between exposures and disease risk. Cases of the disease are often considered a single outcome and assumed to share a common etiology. However, evidence indicates that many human diseases arise and evolve through a range of heterogeneous molecular pathologic processes, influenced by diverse exposures. Pathogenic heterogeneity has been considered in various neoplasms such as colorectal, lung, prostate, and breast cancers, leukemia and lymphoma, and non‐neoplastic diseases, including obesity, type II diabetes, glaucoma, stroke, cardiovascular disease, autism, and autoimmune disease. In this article, we discuss analytic options for studying disease subtype heterogeneity, emphasizing methods for evaluating whether the association of a potential risk factor with disease varies by disease subtype. Methods are described for scenarios where disease subtypes are categorical and ordinal and for cohort studies, matched and unmatched case–control studies, and case–case study designs. For illustration, we apply the methods to a molecular pathological epidemiology study of alcohol intake and colon cancer risk by tumor LINE‐1 methylation subtypes. User‐friendly software to implement the methods is publicly available. Copyright © 2015 John Wiley & Sons, Ltd. |
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| Keywords: | heterogeneity test molecular pathologic epidemiology omics pathogenesis pathology |
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