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Activity Number: 9 - COVID-19 and Survey Sampling: Challenges and Opportunities
Type: Invited
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Survey Research Methods Section
Abstract #315539
Title: Bayesian Analysis of Tests with Unknown Specificity and Sensitivity
Author(s): Andrew Gelman* and Bob Carpenter
Companies: Columbia University and Flatiron Institute
Keywords: Bayesian inference; multilevel regression and poststratification (MRP); specificity; sensitivity; coronavirus

When testing for a rare disease, prevalence estimates can be highly sensitive to un- certainty in the specificity and sensitivity of the test. Bayesian inference is a natural way to propagate these uncertainties, with hierarchical modeling capturing variation in these parameters across experiments. Another concern is the people in the sample not being representative of the general population. Statistical adjustment cannot without strong assumptions correct for selection bias in an opt-in sample, but multilevel regression and poststratification can at least adjust for known differences between the sample and the population. We demonstrate hierarchical regression and poststratification models with code in Stan and discuss their application to a controversial study of SARS-CoV-2 antibodies in a sample of people from the Stanford University area in April 2020.

Authors who are presenting talks have a * after their name.

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