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Related Experiment Videos

Confidence intervals for the excess risk in case-control studies

V Siskind1

  • 1Department of Social and Preventive Medicine, University of Queensland, Australia.

Statistics in Medicine
|July 30, 1996
PubMed
Summary

This study introduces methods to calculate confidence intervals for excess risk, a measure of disease risk due to exposure. The findings aid in understanding disease causation and risk assessment in epidemiological research.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Assessing excess risk is crucial for understanding disease causation attributed to specific exposures.
  • Existing methods for confidence intervals of excess risk, especially with odds ratios from case-control studies, require further development.

Purpose of the Study:

  • To derive and evaluate methods for calculating confidence intervals for excess risk.
  • To address the estimation of excess risk using odds ratios from case-control studies, both with and without covariate adjustment.

Main Methods:

  • The study computes excess risk as a product of incidence rate, etiologic fraction complement, and relative risk minus one.
  • Methods for confidence intervals are derived for excess risk estimation using odds ratios, including logistic regression and unadjusted analyses.

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  • An innovative approach using confidence bounds for exposure parameters is proposed for unadjusted analyses.

    • Main Results:

    • Confidence intervals for excess risk are derived using multiple logistic regression and an unadjusted approach.
    • The performance of these confidence interval systems is assessed through simulation and exact enumeration.
    • Illustrative examples from a case study on agranulocytosis and indomethacin are presented.

    Conclusions:

    • The study provides novel methods for calculating confidence intervals for excess risk, enhancing risk assessment.
    • The derived methods are applicable to case-control studies and offer insights into disease-exposure relationships.
    • The findings contribute to more robust epidemiological analyses and public health risk evaluations.