| Summary: | 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. When the exposure is a continuous variable or a binary variable, a general mismeasurement phenomenon is attenuation in the strength of the relationship between exposure and outcome. However, few have investigated the effect of misclassification on a polychotomous variable. Using Bayesian methods, we investigate how misclassification affects the exposure-disease associations under different settings of classification matrix. Also, we apply a trend test and understand the effect of misclassification according to the power of the test. In addition, since virtually all of work on the impact of exposure misclassification presumes the simplest situation where both the true status and the classified status are binary, my work diverges from the norm, in considering classification into three categories 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 scheme. We focus on the challenging situation whereby no validation data is available from which to infer classification probabilities, but some prior assertions about these probabilities might be justified.
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