Identification of covariates associated with disease is a key part of epidemiological research. Yet, while adjustment for imperfect diagnostic accuracy is well established when estimating disease prevalence, similar adjustment when estimating covariate effects is far less common, although of important practical relevance due to the sensitivity of such analyses to misclassification error. Case-study data exploring evidence for seasonal differences in Salmonella prevalence using serological testing is presented, in addition simulated data with known properties are analysed. It is demonstrated that: (i) adjusting for misclassification error in models comprising continuous covariates can have a very substantial impact on the resulting conclusions which can then be drawn from any analyses; and (ii) incorporating prior knowledge through Bayesian estimation can provide potentially more informative assessments of covariates while removing the assumption of perfect diagnostic accuracy. The method presented is widely applicable and easily generalized to many types of epidemiological studies.