Uncertainty Analysis of Multiple Epidemiological Studies Using Frequency Distributions of Relative Risks

Alexander I. Shlyakhter

Proceed. ISUMA- NAFIPS '95 Third International Symposium on Uncertainty Modeling and Analysis, and Annual Conference of the North American Fuzzy Information Processing Society. IEEE Computing Society Press, 1995, pp. 40-45.


A new format for presenting uncertainty in the results of multiple epidemiologic studies of the same outcome is suggested. A set of 95% confidence intervals for relative risk, RR, is transformed to a frequency distribution of the normalized deviations, ln(RR)/SE(ln(RR)), from the null value ln(RR)=0 (RR=1). I assume that deviations from RR=1 are due to unaccounted residual biases and compare the distribution of these deviations with the distribution of the actual errors in physical measurements where the true values have subsequently become known, and the incidence of large errors can be estimated. Comparison of these distributions can, by analogy, help to understand how convincing is the evidence of elevated risk in observational studies.

Keywords: uncertainty analysis, relative risks, epidemiological studies, observational studies

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