Quantitative Bias Analysis in Regulatory Settings
Quantitative Bias Analysis in Regulatory Settings
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DOI：
10.2105/ajph.2016.303199
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发表时间：
2016-07-01
影响因子：
12.7
通讯作者：
Forshee, Richard A.
中科院分区：
文献类型：
--
作者：
Lash, Timothy L.;Fox, Matthew P.;Forshee, Richard A.
非随机研究在美国食品药品监督管理局（FDA）的上市后活动中至关重要，然而，FDA通常必须在不完善的数据基础上采取行动。系统性错误可能导致不准确的推断，因此开发量化不确定性和偏倚的分析方法并确保在需要时实施这些方法至关重要。“定量偏倚分析”是一个概括性术语，用于定量估计影响关联性测量的系统性误差相关的方向、幅度和不确定性的方法。美国食品药品监督管理局赞助了一个合作项目，旨在开发工具，以更好地量化与监管决策中使用的上市后监测研究相关的不确定性。我们已经描述了这个项目的基本原理，进展和未来的方向。
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.