A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies
Nicola Orsini
Division of Nutritional Epidemiology
Institute of Environmental Medicine
Karolinska Institutet
Stockholm, Sweden
[email protected]
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Rino Bellocco
Department of Statistics
University of Milano-Bicocca
Milano, Italy
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Matteo Bottai
Department of Epidemiology and Biostatistics
Arnold School of Public Health
University of South Carolina
Columbia, SC
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Alicja Wolk
Division of Nutritional Epidemiology
Institute of Environmental Medicine
Karolinska Institutet
Stockholm, Sweden
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Sander Greenland
Departments of Epidemiology and Statistics
University of California, Los Angeles
Los Angeles, CA
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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.
View all articles by these authors:
Nicola Orsini, Rino Bellocco, Matteo Bottai, Alicja Wolk, Sander Greenland
View all articles with these keywords:
episens, episensi, sensitivity analysis, unmeasured confounder, misclassification, bias, epidemiology
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