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Related Experiment Videos

Model-based estimation of population attributable risk under cross-sectional sampling

S Basu1, J R Landis

  • 1Indian Statistical Institute, Calcutta, West Bengal, India.

American Journal of Epidemiology
|December 15, 1995
PubMed
Summary

This study introduces a new method for calculating population attributable risk (PAR) in cross-sectional studies. This approach helps understand disease prevalence reduction by removing risk factors while accounting for other variables.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Population attributable risk (PAR) quantifies the impact of a risk factor on disease prevalence.
  • Existing methods for PAR estimation are primarily for retrospective and cohort studies.
  • Cross-sectional studies require specialized methods for accurate PAR estimation.

Purpose of the Study:

  • To extend model-based approaches for covariate-adjusted population attributable risk (PAR) to cross-sectional study designs.
  • To provide a method for estimating PAR that accounts for covariate effects in cross-sectional data.
  • To illustrate the application of the proposed methods using real-world health data.

Main Methods:

  • Utilized a logit linear model for estimating covariate-adjusted attributable risk.

Related Experiment Videos

  • Employed Taylor series expansions to derive the asymptotic variance of the complex ratio estimate.
  • Incorporated sampling variation of estimated model parameters and risk factor prevalence estimates.

    • Main Results:

    • Successfully extended the model-based PAR estimation to cross-sectional designs.
    • Developed a method to calculate the asymptotic variance for complex ratio estimates in this context.
    • Demonstrated the utility of the methods with cardiovascular disease risk factor data from NHANES II.

    Conclusions:

    • The proposed model-based approach provides a robust method for estimating covariate-adjusted PAR in cross-sectional studies.
    • This methodology enhances the ability to assess the public health impact of risk factors in population-based surveys.
    • The findings are applicable to various epidemiological research settings utilizing cross-sectional data.