Probabilistic Bias Analysis

To this point we have considered situations in which the bias parameters for a bias analysis are known with certainty or modeled as if they are known with certainty (i.e., simple bias analysis, see Chaps. 4– 6). We have also considered models of the impact of combinations of bias parameters on an observed estimate of association (i.e., multidimensional bias analysis, see Chap. 7). Simple bias analysis is an improvement over conventional analyses, which implicitly assume that all the bias parameters are fixed at values that confer no bias. However, the usefulness of simple bias analysis is limited by its assumption that the bias parameters are known without error, a situation that is rarely, if ever, a reality. Multidimensional bias analysis improves on simple bias analysis by examining the impact of more than one set of bias parameters, but even this approach only examines the bias conferred by a limited set of bias parameters. For any analysis, many other possible combinations of bias parameters are plausible, and a multidimensional analysis will not describe the impact of these possibilities. More important, multidimensional analysis gives no sense of which corrected estimate of association is the most likely under the assumed bias model, which can make interpretation of the results challenging.