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

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  • Sander Greenland

Abstract

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.

Suggested Citation

  • Sander Greenland, 2001. "Sensitivity Analysis, Monte Carlo Risk Analysis, and Bayesian Uncertainty Assessment," Risk Analysis, John Wiley & Sons, vol. 21(4), pages 579-584, August.
    • Handle: RePEc:wly:riskan:v:21:y:2001:i:4:p:579-584
    DOI: 10.1111/0272-4332.214136
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    Citations

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    Cited by:

    1. Sander Greenland, 2004. "Bounding Analysis as an Inadequately Specified Methodology," Risk Analysis, John Wiley & Sons, vol. 24(5), pages 1085-1092, October.
    2. Richard R. Lester & Laura C. Green & Igor Linkov, 2007. "Site‐Specific Applications of Probabilistic Health Risk Assessment: Review of the Literature Since 2000," Risk Analysis, John Wiley & Sons, vol. 27(3), pages 635-658, June.
    3. Sander Greenland, 2005. "Multiple‐bias modelling for analysis of observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 267-306, March.
    4. Olivier Catelinois & Dominique Laurier & Pierre Verger & Agnès Rogel & Marc Colonna & Marianne Ignasiak & Denis Hémon & Margot Tirmarche, 2005. "Uncertainty and Sensitivity Analysis in Assessment of the Thyroid Cancer Risk Related to Chernobyl Fallout in Eastern France," Risk Analysis, John Wiley & Sons, vol. 25(2), pages 243-252, April.
    5. Sander Greenland, 2005. "Discussion on "Statistical Issues Arising in the Women's Health Initiative"," Biometrics, The International Biometric Society, vol. 61(4), pages 920-921, December.
    6. Paul Gustafson, 2006. "Sample size implications when biases are modelled rather than ignored," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 865-881, October.

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