Probabilistic Approaches to Better Quantifying the Results of Epidemiologic Studies

Paul Gustafson and Lawrence C. McCandless
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Paul Gustafson: Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, B.C., V6T 1Z2, Canada
Lawrence C. McCandless: Faculty of Health Sciences, Simon Fraser University, 8888 University Drive, Burnaby, B.C., V5A 1S6, Canada

IJERPH, 2010, vol. 7, issue 4, 1-20

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 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2010
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