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Confidence interval estimation of interaction.

D W Hosmer1, S Lemeshow

  • 1School of Public Health, University of Massachusetts, Amherst 01003.

Epidemiology (Cambridge, Mass.)
|September 1, 1992
PubMed
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This study introduces methods for calculating confidence intervals for interaction measures in epidemiology. These methods use standard multiple logistic regression output for better statistical analysis of combined exposure effects.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Interaction measures like relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI) are crucial in epidemiologic studies.
  • Estimating confidence intervals for these interaction measures is essential for assessing statistical significance and reliability.

Purpose of the Study:

  • To present a methodology for obtaining confidence interval estimates for key interaction indices.
  • To demonstrate the utility of routinely available output from multiple logistic regression software for these calculations.

Main Methods:

  • The study focuses on the application of multiple logistic regression models.
  • It details the process of extracting and utilizing model output to compute confidence intervals for RERI, AP, and SI.

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Main Results:

  • The proposed methodology provides a practical approach to confidence interval estimation for interaction measures.
  • Confidence intervals can be reliably obtained using standard statistical software, enhancing the interpretability of interaction effects.

Conclusions:

  • The presented methods facilitate robust statistical inference for interaction in epidemiologic research.
  • Researchers can confidently assess the significance of combined exposure effects using readily available software outputs.