Speaker: Dongxu Wang, UBC

Title: Nondifferential Exposure Misclassification in Case-Control Studies: What should be done with the `Maybe' Exposed? (Slides)

Abstract: There is quite an extensive literature on the deleterious impact of exposure misclassification when inferring exposure-disease associations, and on statistical methods to mitigate this impact. Virtually all of this work, however, presumes a common number of states for the true exposure status and the classified exposure status.  In the simplest situation, for instance, both the true status and the classified status are binary. The present work diverges from the norm, in considering classification into three states when the actual exposure status is simply binary. Intuitively, the classification states might be labeled as `unlikely exposed,' `maybe exposed,' and `likely exposed.' While this situation has been discussed informally in the literature, we provide some theory concerning what can be learned about the exposure-disease relationship, under various assumptions about the classification into the three states.

Keyword: Bayesian methods; case-control analysis;
exposure misclassification; partial identification
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