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Attributable Fractions for Sufficient Cause Interactions

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  • VanderWeele Tyler J

    (Harvard University)

Abstract

A number of results concerning attributable fractions for sufficient cause interactions are given. Results are given both for etiologic fractions (i.e. the proportion of the disease due to a particular sufficient cause) and for excess fractions (i.e. the proportion of disease that could be eliminated by removing a particular sufficient cause). Results are given both with and without assumptions of monotonicity. Under monotonicity assumptions, exact formulas can be given for the excess fraction. When etiologic fractions are of interest or when monotonicity assumptions do not hold for excess fractions then only lower bounds can be given. The interpretation of the results in this paper and in a proposal by Hoffmann et al. (2006) are discussed and compared. A method is described to estimate the lower bounds on attributable fractions using marginal structural models. Identification is discussed in settings in which time-dependent confounding may be present.

Suggested Citation

  • VanderWeele Tyler J, 2010. "Attributable Fractions for Sufficient Cause Interactions," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-28, February.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:5
    DOI: 10.2202/1557-4679.1202
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    References listed on IDEAS

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    1. VanderWeele Tyler J, 2010. "Epistatic Interactions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-24, January.
    2. Tyler J. Vanderweele & James M. Robins, 2008. "Empirical and counterfactual conditions for sufficient cause interactions," Biometrika, Biometrika Trust, vol. 95(1), pages 49-61.
    3. Vansteelandt, Stijn & VanderWeele, Tyler J. & Tchetgen, Eric J. & Robins, James M., 2008. "Multiply Robust Inference for Statistical Interactions," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1693-1704.
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