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A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies

Author

Listed:
  • Nicola Orsini

    (Institute of Environmental Medicine, Karolinska Institutet)

  • Rino Bellocco

    (Department of Statistics, University of Milano-Bicocca)

  • Matteo Bottai

    (Department of Epidemiology and Biostatistics, University of South Carolina)

  • Alicja Wolk

    (Institute of Environmental Medicine, Karolinska Institutet)

  • Sander Greenland

    (Departments of Epidemiology and Statistics, University of California, Los Angeles)

Abstract

Classification errors, selection bias, and uncontrolled confounders are likely to be present in most epidemiologic studies, but the uncertainty introduced by these types of biases is seldom quantified. The authors present a simple yet easy- to-use Stata command to adjust the relative risk for exposure misclassification, selection bias, and an unmeasured confounder. This command implements both deterministic and probabilistic sensitivity analysis. It allows the user to specify a variety of probability distributions for the bias parameters, which are used to simulate distributions for the bias-adjusted exposure – disease relative risk. We illustrate the command by applying it to a case – control study of occupational resin exposure and lung-cancer deaths. By using plausible probability distributions for the bias parameters, investigators can report results that incorporate their uncertainties regarding systematic errors and thus avoid overstating their certainty about the effect under study. These results can supplement conventional results and can help pinpoint ma jor sources of conflict in study interpretations. Copyright 2008 by StataCorp LP.

Suggested Citation

  • Nicola Orsini & Rino Bellocco & Matteo Bottai & Alicja Wolk & Sander Greenland, 2008. "A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies," Stata Journal, StataCorp LP, vol. 8(1), pages 29-48, February.
    • Handle: RePEc:tsj:stataj:v:8:y:2008:i:1:p:29-48
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    Citations

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    Cited by:

    1. Nicola Orsini & Sander Greenland, 2011. "A procedure to tabulate and plot results after flexible modeling of a quantitative covariate," Stata Journal, StataCorp LP, vol. 11(1), pages 1-29, March.
    2. Sander Greenland & Heidi J. Fischer & Leeka Kheifets, 2016. "Methods to Explore Uncertainty and Bias Introduced by Job Exposure Matrices," Risk Analysis, John Wiley & Sons, vol. 36(1), pages 74-82, January.
    3. Paul Gustafson & Lawrence C. McCandless, 2010. "Probabilistic Approaches to Better Quantifying the Results of Epidemiologic Studies," IJERPH, MDPI, vol. 7(4), pages 1-20, April.
    4. John J. Graff & Nalini Sathiakumar & Maurizio Macaluso & George Maldonado & Robert Matthews & Elizabeth Delzell, 2009. "The Effect of Uncertainty in Exposure Estimation on the Exposure-Response Relation between 1,3-Butadiene and Leukemia," IJERPH, MDPI, vol. 6(9), pages 1-20, September.
    5. Sanna Heikkinen & Janne Pitkäniemi & Tytti Sarkeala & Nea Malila & Markku Koskenvuo, 2015. "Does Hair Dye Use Increase the Risk of Breast Cancer? A Population-Based Case-Control Study of Finnish Women," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-14, August.
    6. Patricia Frenz & Jay S. Kaufman & Carolina Nazzal & Gabriel Cavada & Francisco Cerecera & Nicolás Silva, 2017. "Mediation of the effect of childhood socioeconomic position by educational attainment on adult chronic disease in Chile," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 62(9), pages 1007-1017, December.

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