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Statistical methods for estimating attributable risk from retrospective data

A S Whittemore

    Statistics in Medicine
    |July 1, 1982
    PubMed
    Summary
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    This study refines attributable risk calculations to account for confounding factors. New methods provide reliable estimates from case-control data, even with small sample sizes and complex confounder distributions.

    Area of Science:

    • Epidemiology
    • Biostatistics

    Background:

    • Attributable risk measures the proportion of disease caused by a specific exposure.
    • Confounding factors can bias attributable risk estimates.

    Purpose of the Study:

    • To extend Levin's measure of attributable risk to adjust for confounding factors.
    • To develop methods for estimating adjusted attributable risk from case-control data.

    Main Methods:

    • Maximum likelihood estimation for adjusted attributable risk.
    • Computer simulations to evaluate performance with small sample sizes and numerous confounder strata.

    Main Results:

    • Adjusted attributable risk estimates are obtainable from case-control data under specific conditions.
    • Asymptotic standard error estimates performed well, even in small samples.

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  • Bias in estimates was minimal except when exposure prevalence was high among controls, leading to instability.

    • Conclusions:

    • The extended measure provides a robust way to estimate attributable risk while controlling for confounding.
    • Asymptotic standard errors are reliable for confidence interval construction.
    • No single confidence interval method showed a clear advantage in accuracy.