Quantitative bias analysis in regulatory settings

T.L. Lash, M.P. Fox, D. Cooney, Y. Lu and R.A. Forshee

American Journal of Public Health, 2016, vol. 106, issue 7, 1227-1230

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.

Keywords: food and drug administration; human; methodology; organization and management; postmarketing surveillance; procedures; statistical bias; uncertainty; United States, Bias (Epidemiology); Humans; Product Surveillance, Postmarketing; Research Design; Uncertainty; United States; United States Food and Drug Administration (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:aph:ajpbhl:10.2105/ajph.2016.303199_8

DOI: 10.2105/AJPH.2016.303199

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