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Notes on interval estimation of the attributable risk in cross-sectional sampling.

K J Lui1

  • 1Department of Mathematical and Computer Sciences, College of Sciences, San Diego State University, San Diego, CA 92182-7720, USA. kjl@rohan.sdsu.edu

Statistics in Medicine
|June 15, 2001
PubMed
Summary

This study evaluates methods for estimating attributable risk (AR) in cross-sectional studies. Fleiss’s interval estimator performs well for large sample sizes and moderate exposure probabilities, though it may lack efficiency.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Attributable risk (AR) is a key measure for assessing the public health impact of risk factors.
  • Accurate interval estimation of AR is crucial for reliable public health assessments in cross-sectional studies.

Purpose of the Study:

  • To compare the finite-sample performance of five asymptotic interval estimators for attributable risk (AR) in cross-sectional studies.
  • To evaluate coverage probability and average length of AR interval estimators across various scenarios.

Main Methods:

  • Simulation study comparing five asymptotic interval estimators for attributable risk.
  • Analysis focused on coverage probability and average interval length.
  • Scenarios included varying risk ratios, exposure probabilities, and sample sizes.

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Main Results:

  • Leung and Kupper's estimators showed poor coverage when the risk ratio (RR) was 1.
  • Fleiss’s estimator demonstrated good coverage for large sample sizes (>=100) and moderate exposure probabilities (>=0.20), but with potential efficiency loss.
  • Fieller's theorem-based estimator outperformed Wald's for RR >= 2.
  • Leung and Kupper's estimator was preferable for large RR (>=4) and non-small exposure probability (>=0.05).

Conclusions:

  • The choice of interval estimator for attributable risk depends on study parameters like sample size, exposure probability, and risk ratio.
  • Fleiss’s estimator is a robust choice for general use with adequate sample sizes.
  • Specific estimators show advantages under particular conditions (e.g., Fieller's for moderate RR, Leung and Kupper's for high RR).