A Tool for Deterministic and Probabilistic Sensitivity Analysis of Epidemiologic Studies

Abstract

Classification errors, selection bias, and uncontrolled confounders are likely to be present in most epidemiologic studies, but the uncertainty introduced by these types of biases is seldom quantified. The authors present a simple yet easy-to-use Stata command to adjust the relative risk for exposure misclassification, selection bias, and an unmeasured confounder. This command implements both deterministic and probabilistic sensitivity analysis. It allows the user to specify a variety of probability distributions for the bias parameters, which are used to simulate distributions for the bias-adjusted exposure–disease relative risk. We illustrate the command by applying it to a case–control study of occupational resin exposure and lung-cancer deaths. By using plausible probability distributions for the bias parameters, investigators can report results that incorporate their uncertainties regarding systematic errors and thus avoid overstating their certainty about the effect under study. These results can supplement conventional results and can help pinpoint major sources of conflict in study interpretations.