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General relative risk functions for case-control studies.

N E Breslow, B E Storer

    American Journal of Epidemiology
    |July 1, 1985
    PubMed
    Summary
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    This study introduces a flexible family of relative risk models that extend beyond standard multiplicative approaches in epidemiology. These models aid in analyzing complex disease interactions and provide tools for model selection and influence assessment.

    Area of Science:

    • Epidemiologic Methodology
    • Statistical Modeling
    • Biostatistics

    Background:

    • Multiplicative models (log-linear, logistic) are standard in epidemiology.
    • Need for more general model structures, including additive models, is recognized.
    • Existing models may not fully capture complex disease interactions.

    Purpose of the Study:

    • To propose a parametric family of relative risk functions.
    • To provide a framework ranging from subadditive to supramultiplicative relative risks.
    • To facilitate model selection and assess the influence of data points.

    Main Methods:

    • A power transform is applied to the log relative risk to generate a family of models.
    • Graphic analysis of goodness-of-fit statistics aids in model choice.

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  • Intermediate regression diagnostics help evaluate observation influence.

    • Main Results:

    • The proposed family encompasses a wide range of relative risk functions.
    • Methods are demonstrated with random, stratified, and matched case-control samples.
    • Computer software is available for implementing these methods.

    Conclusions:

    • The proposed flexible modeling approach enhances epidemiologic analysis.
    • Distinguishing between different relative risk models requires extensive data.
    • These methods offer valuable tools for understanding multifactorial disease processes.