A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies

Nicola Orsini, Rino Bellocco, Matteo Bottai, Alicja Wolk and Sander Greenland
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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

Stata Journal, 2008, vol. 8, issue 1, 29-48

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 ma jor sources of conflict in study interpretations. Copyright 2008 by StataCorp LP.

Keywords: episens; episensi; sensitivity analysis; unmeasured confounder; misclassification; bias; epidemiology (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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