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Quantitative bias analysis in regulatory settings

Author

Listed:
  • Lash, T.L.
  • Fox, M.P.
  • Cooney, D.
  • Lu, Y.
  • Forshee, R.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.

Suggested Citation

  • Lash, T.L. & Fox, M.P. & Cooney, D. & Lu, Y. & Forshee, R.A., 2016. "Quantitative bias analysis in regulatory settings," American Journal of Public Health, American Public Health Association, vol. 106(7), pages 1227-1230.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2016.303199_8
    DOI: 10.2105/AJPH.2016.303199
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    Cited by:

    1. Brian H. MacGillivray, 2019. "Null Hypothesis Testing ≠ Scientific Inference: A Critique of the Shaky Premise at the Heart of the Science and Values Debate, and a Defense of Value‐Neutral Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 39(7), pages 1520-1532, July.

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