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Adjusting for confounding with statistical software packages.

S M Debanne, R J Sokol

    American Journal of Perinatology
    |April 1, 1986
    PubMed
    Summary
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    This study introduces a new statistical method for clinical research to accurately estimate the impact of prenatal risks on birth outcomes. The novel approach improves effect size calculations in multiple regression analyses.

    Area of Science:

    • Clinical research
    • Biostatistics
    • Epidemiology

    Background:

    • Standard statistical packages are widely used in clinical research.
    • Multiple regression is often employed to analyze prenatal risks and birth outcomes.
    • Stepwise multiple regression can inaccurately attribute shared variance, underestimating effect sizes.

    Purpose of the Study:

    • To present a novel statistical method for more accurate effect size estimation in multivariate analyses.
    • To address the limitations of standard stepwise multiple regression in clinical research.
    • To provide a more appropriate way to assess the impact of independent prenatal risks.

    Main Methods:

    • Adapted an epidemiologic technique (attributable risk) for multivariate settings.
    • Applied the novel method within a standard statistical package.

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  • Demonstrated the method with an example using prenatal risks and birthweight for gestational age.

    • Main Results:

    • The novel method provides more appropriate estimates of effect sizes for independent risks.
    • This technique corrects for the underestimation of effect sizes in stepwise multiple regression.
    • Shared variance is more accurately attributed to relevant independent variables.

    Conclusions:

    • The adapted attributable risk method offers improved effect size estimation in clinical research.
    • This approach enhances the accuracy of multivariate analyses for prenatal risk factors.
    • Researchers can utilize this method for more precise understanding of independent risk impacts on outcomes.