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Large sample confidence intervals for regression standardized risks, risk ratios, and risk differences.

W D Flanders, P H Rhodes

    Journal of Chronic Diseases
    |January 1, 1987
    PubMed
    Summary
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    This study presents methods for calculating approximate confidence limits for standardized risks and risk ratios derived from logistic regression. These techniques offer an alternative to direct standardization, especially with multiple covariates.

    Area of Science:

    • Biostatistics
    • Epidemiology
    • Statistical Modeling

    Background:

    • Standardizing risks, risk ratios, and risk differences is crucial in epidemiological studies.
    • Direct standardization is a common method but can be cumbersome with numerous covariates.
    • Logistic regression offers a flexible modeling approach for risk estimation.

    Purpose of the Study:

    • To present methods for calculating approximate confidence limits for standardized measures derived from logistic regression.
    • To provide an alternative to direct standardization, particularly when dealing with many covariates.
    • To illustrate the application and comparison of these methods using published data.

    Main Methods:

    • Utilized logistic regression to model risk.
    • Developed methods for calculating approximate confidence limits for standardized risks, risk ratios, and risk differences.

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  • Employed a published dataset for empirical illustration.

    • Main Results:

    • Presented novel methods for confidence limit calculation for standardized measures from logistic regression.
    • Demonstrated the utility of these methods through a practical example.
    • Allowed for comparison with confidence limits obtained via direct standardization.

    Conclusions:

    • The proposed methods offer a viable approach for quantifying uncertainty in standardized measures from logistic regression.
    • These techniques are particularly advantageous in scenarios with multiple confounding variables.
    • The study provides practical tools for researchers in biostatistics and epidemiology.