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Activity Number: 477
Type: Topic Contributed
Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #315680 View Presentation
Title: Bayesian Adjustment for Measurement Error: Under What Conditions Is Adjustment Effective?
Author(s): Paul Gustafson*
Companies: The University of British Columbia
Keywords: Bayesian inference ; measurement error
Abstract:

In broad strokes, Bayesian adjustment for measurement error in epidemiological contexts can be straightforward. With exposure measurement error, for instance, one can hierarchically bring together a model for the outcome given the error-free (but latent) exposure, a model for this exposure itself, and a model for the measurement error. Turning the Bayesian crank then does the rest, yielding inferences about parameters in the model for the outcome given the error-free exposure. Often, however, some subtlety lies in the question of how much must be known or assumed for this adjusted inference to be effective. One issue surrounds assumptions about structure, such as whether the measurement error can be assumed to be nondifferential. Another issue surrounds the strength of a priori knowledge about the magnitude of measurement error, or alternately the availability of validation data with which to infer this magnitude. In this talk then, we take a detailed look at the circumstances under which adjustment for measurement error is worthwhile.


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