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Binomial regression in GLIM: estimating risk ratios and risk differences.

S Wacholder

    American Journal of Epidemiology
    |January 1, 1986
    PubMed
    Summary
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    Logistic regression estimates odds ratios, but new methods using generalized linear models can now estimate risk ratios and risk differences for binomial data, adjusting for confounders.

    Area of Science:

    • Biostatistics
    • Epidemiology
    • Statistical Modeling

    Background:

    • Logistic regression commonly estimates odds ratios adjusted for covariates.
    • Estimating other risk measures like risk ratio and risk difference multivariately for prospective binomial data has been challenging.
    • Existing methods lack simplicity for calculating these alternative parameters.

    Purpose of the Study:

    • To present a simple method for estimating risk ratios and risk differences from prospective binomial data.
    • To extend multivariate analysis beyond odds ratios in logistic regression.
    • To provide tools for comprehensive risk assessment in epidemiological studies.

    Main Methods:

    • Utilizing the generalized linear model (GLM) framework.
    • Employing different link functions or data transformations within GLM.

    Related Experiment Videos

  • Developing macros for the GLIM statistical software to implement the method.

    • Main Results:

    • The proposed method allows for the multivariate estimation of risk ratios and risk differences.
    • These parameters can be calculated while adjusting for confounding factors.
    • The approach is demonstrated using a previously published data set.

    Conclusions:

    • A straightforward method is now available for estimating risk ratios and risk differences in multivariate prospective binomial regression.
    • This facilitates a more complete analysis of risk beyond the odds ratio.
    • The GLIM macros offer practical utility for researchers in biostatistics and epidemiology.