Abstract:
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In some situations, there is quite a clean story on how measurement error or misclassification, if ignored, can distort inferences about associations of interest. And commensurately the approach to adjusting for measurement error is straightforward and intuitive, provided amenable information about the measurement error is available (say in the form of a validation study). Other problems are more complex, with a less predictable story arc. We give an example of such an arc, in a problem involving administrative health data. Here the ascertainment of disease status (control or case) is error-prone, yet this ascertainment is used as a matching factor in the creation of a for-research dataset. Using Bayesian analysis and multiple sources of information it is possible to adjust for the imperfect ascertainment of disease status.
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