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Probabilistic Approaches to Better Quantifying the Results of Epidemiologic Studies

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  • Paul Gustafson

    (Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, B.C., V6T 1Z2, Canada)

  • Lawrence C. McCandless

    (Faculty of Health Sciences, Simon Fraser University, 8888 University Drive, Burnaby, B.C., V5A 1S6, Canada)

Abstract

Typical statistical analysis of epidemiologic data captures uncertainty due to random sampling variation, but ignores more systematic sources of variation such as selection bias, measurement error, and unobserved confounding. Such sources are often only mentioned via qualitative caveats, perhaps under the heading of ‘study limitations.’ Recently, however, there has been considerable interest and advancement in probabilistic methodologies for more integrated statistical analysis. Such techniques hold the promise of replacing a confidence interval reflecting only random sampling variation with an interval reflecting all, or at least more, sources of uncertainty. We survey and appraise the recent literature in this area, giving some prominence to the use of Bayesian statistical methodology.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:7:y:2010:i:4:p:1520-1539:d:7770
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    References listed on IDEAS

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    1. Greenland S., 2003. "The Impact of Prior Distributions for Uncontrolled Confounding and Response Bias: A Case Study of the Relation of Wire Codes and Magnetic Fields to Childhood Leukemia," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 47-54, January.
    2. Paul Gustafson & Nhu D. Le & Refik Saskin, 2001. "Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities," Biometrics, The International Biometric Society, vol. 57(2), pages 598-609, June.
    3. 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.
    4. 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.
    5. Rosenbaum, Paul R., 2005. "Heterogeneity and Causality: Unit Heterogeneity and Design Sensitivity in Observational Studies," The American Statistician, American Statistical Association, vol. 59, pages 147-152, May.
    6. Paul Gustafson & Nhu D. Le, 2002. "Comparing the Effects of Continuous and Discrete Covariate Mismeasurement, with Emphasis on the Dichotomization of Mismeasured Predictors," Biometrics, The International Biometric Society, vol. 58(4), pages 878-887, December.
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