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- Title
Quantitative Bias Analysis in Regulatory Settings.
- Authors
Lash, Timothy L.; Fox, Matthew P.; Cooney, Darryl; Yun Lu; Forshee, Richard A.
- 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.
- Subjects
DECISION making; EXPERIMENTAL design; RESEARCH methodology; MEDICAL research; QUALITY assurance; UNITED States. Food &; Drug Administration; DATA analysis; RESEARCH bias; STANDARDS
- Publication
American Journal of Public Health, 2016, Vol 106, Issue 7, p1227
- ISSN
0090-0036
- Publication type
Article
- DOI
10.2105/AJPH.2016.303199