Probabilistic bias analysis of epidemiological results
Classification errors, selection bias, and uncontrolled confounding are likely to be present in most epidemiological studies, but the uncertainty introduced by this type of biases is seldom quantified. The authors present a simple yet easy-to-use method to adjust the relative risk of a disease for misclassification of a binary exposure, selection bias, and unmeasured confounding variable. The accompanying Stata tool implements both ordinary and probabilistic sensitivity analysis. It allows the user to specify a variety of probability densities for the bias parameters, and use these densities to obtain simulation limits for the bias adjusted exposure-disease relative risk. The authors illustrate the method by applying it to a published positive association between occupational resin exposure and lung-cancer deaths in a case-control study. By employing plausible probability distributions for the bias parameters, investigators can report results that incorporate their uncertainties regarding unmeasured or uncontrolled confounding, and thus avoid overstating their certainty about the effect under study. These results can usefully supplement standard data descriptions and conventional results.
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