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Quantitative Bias Analysis for Collaborative Science.

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We used the study by Forns and colleagues1 to outline how quantitative bias analysis (QBA) can be applied to collaborative science projects. Our objective was to quantify the conditions necessary… Click to show full abstract

We used the study by Forns and colleagues1 to outline how quantitative bias analysis (QBA) can be applied to collaborative science projects. Our objective was to quantify the conditions necessary to yield the observed cohort-specific effect estimates in scenarios when: [1] air pollution has no effect on attentiondeficit/hyperactivity disorder (ADHD) risk, or [2] air pollution increases the risk of ADHD. We examined three classes of bias—differential misclassification, differential selection, and uncontrolled confounding. Where possible we used the reported data and based our assumptions on putative mechanisms of bias specific to the subject matter.

Keywords: bias analysis; collaborative science; quantitative bias

Journal Title: Epidemiology
Year Published: 2018

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