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Uncertainty analysis: an example of its application to estimating a survey proportion
Anne M Jurek1, George Maldonado, Sander Greenland
1Department of Pediatrics, University of Minnesota, Mayo Mail Code 715, 420 Delaware St SE, Minneapolis, MN 55455, USA. jure0007@umn.edu
Journal of Epidemiology and Community Health
|June 15, 2007
Summary
Uncertainty analysis quantifies biases in epidemiological studies. This method, new to epidemiology, uses four steps to assess and propagate uncertainty for more reliable results.
Area of Science:
- Epidemiology
- Biostatistics
- Quantitative Science
Background:
- Uncertainty analysis is established in engineering and policy analysis.
- It is a relatively new method for quantitative bias assessment in epidemiology.
- Epidemiological study results often contain biases that require careful evaluation.
Purpose of the Study:
- To introduce and illustrate the application of uncertainty analysis in epidemiology.
- To provide a framework for quantitatively assessing biases in epidemiological research.
- To demonstrate how uncertainty analysis can improve the reliability of epidemiological findings.
Main Methods:
- Specifying the target parameter and its estimator's equation.
- Defining equations for random and bias effects on the estimator.
- Specifying prior probability distributions for bias parameters.
- Employing Monte-Carlo or analytic techniques to propagate uncertainty.
Main Results:
- Demonstrated a four-step process for uncertainty analysis.
- Applied the method to estimate four proportions from epidemiological literature.
- Obtained approximate posterior probability distributions for the parameters of interest.
Conclusions:
- Uncertainty analysis offers a robust quantitative approach to bias assessment in epidemiology.
- The method enhances the understanding and reporting of uncertainty in study findings.
- Further application of uncertainty analysis can strengthen the validity of epidemiological evidence.
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