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Int. J. Environ. Res. Public Health 2010, 7(4), 1520-1539; doi:10.3390/ijerph7041520

Probabilistic Approaches to Better Quantifying the Results of Epidemiologic Studies

1
Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, B.C., V6T 1Z2, Canada
2
Faculty of Health Sciences, Simon Fraser University, 8888 University Drive, Burnaby, B.C., V5A 1S6, Canada
*
Author to whom correspondence should be addressed.
Received: 12 February 2010 / Revised: 26 March 2010 / Accepted: 29 March 2010 / Published: 1 April 2010
(This article belongs to the Special Issue Advances in Epidemiology)
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Abstract

Typical statistical analysis of epidemiologic data captures uncertainty due to random sampling variation, but ignores more systematic sources of variation such as selection bias, measurement error, and unobserved confounding. Such sources are often only mentioned via qualitative caveats, perhaps under the heading of ‘study limitations.’ Recently, however, there has been considerable interest and advancement in probabilistic methodologies for more integrated statistical analysis. Such techniques hold the promise of replacing a confidence interval reflecting only random sampling variation with an interval reflecting all, or at least more, sources of uncertainty. We survey and appraise the recent literature in this area, giving some prominence to the use of Bayesian statistical methodology.
Keywords: confounding; epidemiologic methods; exposure misclassification; selection bias; sensitivity analysis confounding; epidemiologic methods; exposure misclassification; selection bias; sensitivity analysis
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Gustafson, P.; McCandless, L.C. Probabilistic Approaches to Better Quantifying the Results of Epidemiologic Studies. Int. J. Environ. Res. Public Health 2010, 7, 1520-1539.

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