title: Positing, fitting, and selecting regression models for pooled biomarker data.
abstract: Pooling biospecimens prior to performing lab assays can help reduce lab costs, preserve specimens, and reduce information loss when subject to a limit of detection. Because many biomarkers measured in epidemiological studies are positive and right-skewed, proper analysis of pooled specimens requires special methods. In this paper, we develop and compare parametric regression models for skewed outcome data subject to pooling, including a novel parameterization of the gamma distribution that takes full advantage of the gamma summation property. We also develop a Monte Carlo approximation of Akaike's Information Criterion applied to pooled data in order to guide model selection. Simulation studies and analysis of motivating data from the Collaborative Perinatal Project suggest that using Akaike's Information Criterion to select the best parametric model can help ensure valid inference and promote estimate precision.
authors.affiliation: Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, 20892, MD, U.S.A.
authors.forename: Emily M
authors.initials: EM
authors.lastname: Mitchell
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, 30322, GA, U.S.A.
authors.forename: Robert H
authors.identifier: http://orcid.org/0000-0001-7883-6404
authors.initials: RH
authors.lastname: Lyles
authors.affiliation: Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, 20892, MD, U.S.A.
authors.forename: Enrique F
authors.initials: EF
authors.lastname: Schisterman
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis*; D056808:Biostatistics / Q000379:methods; D054357:Chemokine CXCL10 / Q000032:analysis; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007265:Inhibins / Q000097:blood; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D011247:Pregnancy; D011256:Pregnancy Outcome; D012044:Regression Analysis
chemical_list: D015415:Biomarkers; C114753:CXCL10 protein, human; D054357:Chemokine CXCL10; D007265:Inhibins
keywords: AIC; MCEM; biomarkers; gamma; pooled specimens; skewness
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