Skip to main content

Sensitivity Analysis, Monte Carlo Risk Analysis, and Bayesian Uncertainty Assessment

Buy Article:

$43.00 plus tax (Refund Policy)

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.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Bayesian analysis; Monte Carlo analysis; epidemiologic methods; relative risk; risk assessment

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

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more