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Related Experiment Videos

Software for attributable risk and confidence interval estimation in case-control studies

M Mezzetti1, M Ferraroni, A Decarli

  • 1Istituto di Statistica Medica e Biometria, Università degli Studi di Milano, Italy.

Computers and Biomedical Research, an International Journal
|February 1, 1996
PubMed
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This study introduces an SAS macro for estimating attributable risk (AR) and confidence intervals in case-control studies, particularly useful for multiple risk and confounding factors. The tool aids in analyzing disease-risk factor relationships using logistic regression.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Software

Background:

  • Estimating attributable risk (AR) is crucial for public health, especially with multiple risk and confounding factors.
  • Existing methods may not adequately address complex scenarios in case-control studies.
  • There is a need for accessible tools to compute AR and confidence intervals.

Purpose of the Study:

  • To develop a user-friendly SAS macro for calculating model-based attributable risk (AR) and confidence intervals.
  • To implement the Benichou and Gail procedure for case-control data analysis.
  • To provide a reliable tool for assessing the relationship between risk factors and disease.

Main Methods:

  • Development of an SAS macro based on the Benichou and Gail procedure.

Related Experiment Videos

  • Utilizes unconditional logistic regression for case-control studies with simple random sampling of controls.
  • Employs the delta method to calculate AR variance, incorporating probability and parameter estimates.

    • Main Results:

    • The SAS macro provides estimates of relative risks and attributable risk (AR) for exposure factors.
    • It generates 95% confidence intervals for the attributable risk.
    • The macro requires minimal user input, focusing on work file and variable names.

    Conclusions:

    • The developed SAS macro offers a practical solution for estimating attributable risk in case-control studies.
    • It is suitable for analyses involving multiple risk and confounding factors.
    • The macro has been validated on published case-control study data for reliability.