Sensitivity Analysis, Monte Carlo Risk Analysis, and Bayesian Uncertainty Assessment
Standard statistical methods understate the uncertainty one should attach to effect estimates obtained from observational data. Among the methods used to address this problem are sensitivity analysis, Monte Carlo risk analysis (MCRA), and Bayesian uncertainty assessment. Estimates from MCRAs have been presented as if they were valid frequentist or Bayesian results, but examples show that they need not be either in actual applications. It is concluded that both sensitivity analyses and MCRA should begin with the same type of prior specification effort as Bayesian analysis.
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Document Type: Original Article
Affiliations: Department of Epidemiology, UCLA School of Public Health, and Department of Statistics, UCLA College of Letters and Science, Los Angeles, CA 90095-1772.
Publication date: 01 August 2001