title: Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies.
abstract: Matched case-control studies are popular designs used in epidemiology for assessing the effects of exposures on binary traits. Modern studies increasingly enjoy the ability to examine a large number of exposures in a comprehensive manner. However, several risk factors often tend to be related in a nontrivial way, undermining efforts to identify the risk factors using standard analytic methods due to inflated type-I errors and possible masking of effects. Epidemiologists often use data reduction techniques by grouping the prognostic factors using a thematic approach, with themes deriving from biological considerations. We propose shrinkage-type estimators based on Bayesian penalization methods to estimate the effects of the risk factors using these themes. The properties of the estimators are examined using extensive simulations. The methodology is illustrated using data from a matched case-control study of polychlorinated biphenyls in relation to the etiology of non-Hodgkin's lymphoma.
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York 10017, U.S.A.
authors.forename: Jaya M
authors.initials: JM
authors.lastname: Satagopan
authors.affiliation: Departments of Family Medicine and Biostatistics, University of Michigan, Ann Arbor, Michigan 48104, U.S.A.
authors.forename: Ananda
authors.initials: A
authors.lastname: Sen
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York 10017, U.S.A.
authors.forename: Qin
authors.initials: Q
authors.lastname: Zhou
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Insitute, Rockville, Maryland 20892, U.S.A.
authors.forename: Qing
authors.initials: Q
authors.lastname: Lan
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Insitute, Rockville, Maryland 20892, U.S.A.
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Rothman
authors.affiliation: Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo 0304, Norway.
authors.forename: Hilde
authors.initials: H
authors.lastname: Langseth
authors.affiliation: Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.
authors.forename: Lawrence S
authors.initials: LS
authors.lastname: Engel
mesh_terms: D001699:Biometry / Q000379:methods; D016022:Case-Control Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008228:Lymphoma, Non-Hodgkin / Q000139:chemically induced; D015233:Models, Statistical; D011078:Polychlorinated Biphenyls / Q000009:adverse effects; D012044:Regression Analysis
chemical_list: D011078:Polychlorinated Biphenyls
keywords: Bayesian lasso; Bayesian ridge; Empirical Bayes; Hierarchical Bayes; Non-Hodgkin's lymphoma; Two-stage lasso