Quantitative Bias Analysis in Regulatory Settings.
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 bia...
| Published in: | American Journal of Public Health Vol. 106; no. 7; pp. 1227 - 1231 |
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| Main Authors: | , , , , |
| Format: | Article |
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American Public Health Association
Jul2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=116097694&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 116097694 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00900036 APH jtl: American Journal of Public Health issn: 00900036 maglogo: N pubinfo: dt: Jul2016 vid: 106 iid: 7 pid: 44 pub: American Public Health Association artinfo: ui: 116097694 10.2105/AJPH.2016.303199 ppf: 1227 ppct: 4 formats: tig: atl: Quantitative Bias Analysis in Regulatory Settings. aug: au: Lash, Timothy L. Fox, Matthew P. Cooney, Darryl Yun Lu Forshee, Richard A. affil: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA Department of Epidemiology, Boston University School of Public Health, Boston, MA SciMetrika LLC, Durham, NC Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD su: United States. Food & Drug Administration Decision making Medical research Experimental design Research methodology Quality assurance Data analysis Research bias Standards sug: subj: Decision making Medical research United States. Food & Drug Administration Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology) Experimental design Research methodology Quality assurance Data analysis Research bias Standards ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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