title: Quantitative Bias Analysis in Regulatory Settings.
abstract: Nonrandomized studies are essential in the postmarket activities of the US Food and Drug Administration, which, however, must often act on the basis of imperfect data. Systematic errors can lead to inaccurate inferences, so it is critical to develop analytic methods that quantify uncertainty and bias and ensure that these methods are implemented when needed. "Quantitative bias analysis" is an overarching term for methods that estimate quantitatively the direction, magnitude, and uncertainty associated with systematic errors influencing measures of associations. The Food and Drug Administration sponsored a collaborative project to develop tools to better quantify the uncertainties associated with postmarket surveillance studies used in regulatory decision making. We have described the rationale, progress, and future directions of this project.
authors.affiliation: Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD.
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Lash
authors.affiliation: Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD.
authors.forename: Matthew P
authors.initials: MP
authors.lastname: Fox
authors.affiliation: Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD.
authors.forename: Darryl
authors.initials: D
authors.lastname: Cooney
authors.affiliation: Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD.
authors.forename: Yun
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Timothy L. Lash is with the Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. Matthew P. Fox is with the Department of Epidemiology, Boston University School of Public Health, Boston, MA. At the time of the study, Darryl Cooney was with SciMetrika LLC, Durham, NC. Yun Lu and Richard A. Forshee are with the Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD.
authors.forename: Richard A
authors.initials: RA
authors.lastname: Forshee
mesh_terms: D015982:Bias; D006801:Humans; D011358:Product Surveillance, Postmarketing / Q000379:methods*; D012107:Research Design; D035501:Uncertainty; D014481:United States; D014486:United States Food and Drug Administration / Q000458:organization & administration*