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Standard errors for attributable risk for simple and complex sample designs.

Barry I Graubard1, Thomas R Fears

  • 1Biostatistics Branch, National Cancer Institute, 6120 Executive Boulevard, Room 8024, Bethesda, Maryland 20892, USA. graubarb@mail.nih.gov

Biometrics
|September 2, 2005
PubMed
Summary
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Estimating adjusted attributable risk (AR) is crucial for public health. This study introduces variance estimation methods for AR using logistic regression and influence functions, applicable to diverse study designs and complex survey data.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Survey Methodology

Background:

  • Adjusted attributable risk (AR) quantifies the proportion of disease attributable to a specific exposure.
  • Accurate variance estimation is essential for reliable AR estimates, especially in complex survey data.

Purpose of the Study:

  • To develop and present simple, programmable expressions for estimating the variance of adjusted attributable risk (AR).
  • To demonstrate the applicability of these variance estimators across various study designs and complex sampling strategies.

Main Methods:

  • Utilized influence function methods from survey sampling theory.
  • Applied logistic regression to obtain odds ratios for adjusted AR estimation.
  • Developed variance estimators compatible with case-control, cross-sectional, and cohort studies, including matched and complex survey designs.

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Main Results:

  • Provided easily programmable expressions for variance estimation of adjusted AR.
  • Demonstrated the utility of the methods with real-world examples from NHANES III (childhood asthma) and a melanoma case-control study.

Conclusions:

  • The proposed variance estimation methods are versatile and applicable to a wide range of epidemiological studies.
  • These methods facilitate robust statistical inference for adjusted attributable risk in diverse research settings.