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Confidence intervals for measures of interaction

S F Assmann1, D W Hosmer, S Lemeshow

  • 1Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amberse, USA.

Epidemiology (Cambridge, Mass.)
|May 1, 1996
PubMed
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This study compares methods for measuring disease interaction, finding bootstrap confidence intervals offer the most accurate estimation for relative excess risk and attributable proportion.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Interaction in disease is defined as a departure from additive risk models.
  • Quantifying interaction is crucial for understanding disease etiology and public health interventions.
  • Relative excess risk due to interaction (RERI) and attributable proportion due to interaction (AP) are key measures.

Purpose of the Study:

  • To compare the performance of different confidence interval estimation techniques for measures of interaction.
  • To evaluate methods including delta method and various bootstrap confidence intervals.
  • To identify the most reliable method for estimating interaction measures in case-control studies.

Main Methods:

  • Utilized simulated case-control data to assess confidence interval estimation techniques.
  • Compared a delta method-based interval with three bootstrap confidence interval approaches.

Related Experiment Videos

  • Applied the estimation methods to real-world case-control study data.

    • Main Results:

    • One bootstrap confidence interval method demonstrated superior performance.
    • This method exhibited coverage closest to the nominal level.
    • It also showed better balance in interval accuracy, minimizing directional bias.

    Conclusions:

    • Bootstrap confidence intervals provide a more accurate estimation for interaction measures like RERI and AP.
    • The preferred bootstrap method offers improved reliability for assessing synergistic effects in epidemiological studies.
    • Findings aid in selecting optimal statistical methods for interaction analysis in public health research.