Uncertainty analysis is a method, established in engineering and policy analysis but relatively new to epidemiology, for the quantitative assessment of biases in the results of epidemiological studies. Each uncertainty analysis is situation specific, but usually involves four main steps: (1) specify the target parameter of interest and an equation for its estimator; (2) specify the equation for random and bias effects on the estimator; (3) specify prior probability distributions for the bias parameters; and (4) use Monte-Carlo or analytic techniques to propagate the uncertainty about the bias parameters through the equation, to obtain an approximate posterior probability distribution for the parameter of interest. A basic example is presented illustrating uncertainty analyses for four proportions estimated from a survey of the epidemiological literature.
- CL, confidence limit
- EME, exposure-measurement error
- FN, false negative
- FP, false positive
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Funding: This research has been supported by a grant from the US Environmental Protection Agency’s Science to Achieve Results (STAR) program.
Competing interests: None.
Disclaimer: Although the research described in the article has been funded in part by the US Environmental Protection Agency’s STAR program through grant (U-91615801-0), it has not been subject to any EPA review and, therefore, does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.