Role of Bayes in more realistic inferences about exposure-disease relationships

One task that Bayesian methodology can accomplish in the epidemiologic context of inferring exposure-disease relationships is the replacement of more dubious assumptions with less dubious assumptions. That is, a prior distribution can be constructed to state than a standard assumption is not likely to be strongly violated, rather than stating that the assumption holds exactly. For instance, one can replace an assumption of no unmeasured confounders with an assumption that there are not likely to be strong confounders which aren't measured. One can replace an assumption that exposure assessment is perfect with an assumption that exposure assessment is likely very good. Or one can replace an assumption that exposure is misclassified with known misclassification probabilities with an assumption that the supplied values are good guesses for the misclassification probabilities. This talk will survey the pros and cons of employing Bayesian methods in this way.