Nicola Orsini () (Institute of Environmental Medicine, Karolinska Institutet) Rino Bellocco (Department of Statistics, University of Milano-Bicocca) Matteo Bottai (Department of Epidemiology and Biostatistics, University of South Carolina) Alicja Wolk (Institute of Environmental Medicine, Karolinska Institutet) Sander Greenland (Departments of Epidemiology and Statistics, University of California, Los Angeles)
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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 ma jor sources of conflict in study interpretations. Copyright 2008 by StataCorp LP.
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Article provided by StataCorp LP in its journal Stata Journal.
Volume (Year): 8 (2008) Issue (Month): 1 (February) Pages: 29-48 Download reference. The following formats are available: HTML
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