Various sensitivity analyses techniques use basic and matrix algebra to assess and correct for differential, non-differential, or simultaneous misclassification of exposure and disease on epidemiologic measures of association. Predictive values are also used to adjust relative risk estimates and to correct for biases resulting from misclassification of outcome status. In some instances, computer programs are used to perform more extensive analyses. While these established techniques for conducting a â€œformalâ€ sensitivity analysis are valuable, there are several important reasons why these comparisons may not be carried out. First, reliable estimates of sensitivity, specificity, and true disease frequency are often required, but may not be available. Second, these methods make assumptions about the data such as misclassification of only the outcome variable, sensitivity and specificity parameters that are the same for each comparison group, or misclassification that is considered in isolation from other forms of bias, such as selection bias or confounding. Third, these methods are not standardized and may be useful only with particular study designs, further hampering their appropriate use. Finally, the methodology is complex, such that most public health professionals or clinicians cannot undertake a sensitivity analysis without formal training in epidemiology or statistics.