Thursday, February 14, 2019; 1:15 PM - 3:00 PM
Section outline
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Live Lecture: Quantitative Bias Analysis (QBA)
The focus of this workshop is methods for conducting quantitative bias analysis in epidemiologic research. We will first provide a brief introduction to confounding, information bias, and selection bias using causal diagrams. Next, we will discuss deterministic and probabilistic bias analysis. Dr. Mayeda will discuss examples of specific biases in her own work on determinants of cognitive decline in older adults. At the end of the workshop, participants should walk away with an understanding of the motivations behind quantitative bias analysis and how to use tools for conducting quantitative bias analysis.
Please RSVP to event: https://bit.ly/2FrT8rt
Faculty: Elizabeth Rose Mayeda
Location: Mission Hall 1401
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Orsini article for QBA HW File
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Hernán MA, Hernández-DÃaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615-625. File
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Fox MP, Lash TL, Greenland S. A method to automate probabilistic sensitivity analyses of misclassified binary variables. International journal of epidemiology. 2005;34(6):1370-1376. File
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Last TL, et al. Good practices for quantitative bias analysis. International Journal of Epidemiology. 2014;43(6):1969-1985. File
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Mayeda ER, et al. A simulation platform for quantifying survival bias: an application to research on determinants of cognitive decline. American Journal of Epidemiology. 2016 Sep 1;184(5):378-87. File
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Mayeda ER, et al. Does selective survival prior to study enrolment attenuate estimated effects of education on rate of cognitive decline in older adults? A simulation approach for quantifying survival bias in life course epidemiology. File
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Mayeda ER, et al. Can survival bias explain the age attenuation of racial inequalities in stroke incidence? A simulation study. Epidemiology. 2018 Jul;29(4):525-532. File