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Effects of interaction, confounding and observational error on attributable risk estimation.

S D Walter

    American Journal of Epidemiology
    |May 1, 1983
    PubMed
    Summary

    Attributable risk estimation is complex with multiple interacting risk factors. This study identifies conditions for valid risk assessment and additive public health effects, even with confounding and interaction.

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    Statistical methods in medical research·2018

    Area of Science:

    • Epidemiology
    • Biostatistics
    • Public Health

    Background:

    • Assessing attributable risk is crucial for understanding disease burden from specific risk factors.
    • Interactions and confounding among multiple risk factors complicate attributable risk estimation.
    • Misclassification of exposure can introduce bias into attributable risk calculations.

    Purpose of the Study:

    • To define conditions under which attributable risk estimates are valid in the presence of interacting or confounded risk factors.
    • To explore the additivity of public health effects for multiple risk factors.
    • To investigate the impact of exposure misclassification on attributable risk estimates.

    Main Methods:

    • Mathematical modeling of attributable risk under various scenarios of risk factor interaction and confounding.
    • Analysis of conditions for constant attributable risk among the exposed.
    • Evaluation of marginal attributable risk estimates and additive public health effects.
    • Examination of bias due to exposure misclassification, including false positive and false negative rates.

    Main Results:

    • Conditions for constant, valid, and additive attributable risk estimates were identified, varying with the number of risk factors.
    • For exactly two binary risk factors, these conditions are both sufficient and necessary.
    • Exposure misclassification, particularly insensitivity errors, can bias attributable risk estimates.
    • Unbiased estimates are achievable with specific false negative and false positive rates, albeit with increased standard error.

    Conclusions:

    • The study provides a framework for accurate attributable risk assessment in complex epidemiological settings.
    • Understanding the interplay of confounding and interaction is key to valid risk factor evaluation.
    • Careful consideration of exposure data quality is essential to minimize bias in public health impact assessments.

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