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Estimation of attributable risk for case-control studies with multiple matching.

Kung-Jong Lui1

  • 1Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182-7720, USA. kjl@rohan.sdsu.edu

Statistics in Medicine
|September 9, 2005
PubMed
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This study evaluates attributable risk interval estimators for case-control studies with few matched sets. New methods improve coverage probability and efficiency compared to existing estimators, especially in small sample sizes.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Case-control studies with multiple matching are common in epidemiology.
  • Existing attributable risk interval estimators may perform poorly with small or moderate numbers of matched sets.

Purpose of the Study:

  • To evaluate the performance of the Kuritz and Landis interval estimator for attributable risk in small/moderate matched sets.
  • To compare its performance against four other interval estimators.
  • To identify improved estimators for specific scenarios.

Main Methods:

  • Monte Carlo simulation was used to assess interval estimator performance.
  • Coverage probability and efficiency were key performance metrics.
  • Comparison involved the Kuritz and Landis estimator, log-transformed, logit-transformed, and two quadratic equation-derived estimators.

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Main Results:

  • Kuritz and Landis estimator showed reduced coverage probability with large exposure probability among cases.
  • Log-transformed and quadratic equation-derived estimators improved coverage, particularly for small matched sets.
  • Quadratic equation-derived estimators were consistently more efficient than Kuritz and Landis' estimator.

Conclusions:

  • New interval estimators, especially those from quadratic equations, offer improved performance for attributable risk in case-control studies with limited matched sets.
  • The choice of estimator depends on the probability of exposure and the odds ratio.
  • These findings enhance the reliability of risk estimation in epidemiological research.