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Attributable risk estimation from case-control data via logistic regression.

K Drescher1, W Schill

  • 1Institute of Statistics, University of Bremen, Germany.

Biometrics
|December 1, 1991
PubMed
Summary
This summary is machine-generated.

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This study introduces a method to estimate population attributable risk using logistic regression on case-control data. The approach allows for calculation of joint, stratum-specific, and summary risks and their variances.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Research

Background:

  • Estimating the population attributable risk (PAR) is crucial for understanding the impact of risk factors on disease burden.
  • Traditional methods for PAR estimation can be complex, especially with unmatched or stratified case-control data.

Purpose of the Study:

  • To develop a straightforward method for estimating joint population attributable risk from unmatched case-control data.
  • To extend this method for calculating stratum-specific and summary population attributable risks from stratified data.

Main Methods:

  • Fitting an unconditional logistic regression model to unmatched case-control data.
  • Utilizing the intercept parameter and its asymptotic variance for risk estimation.
  • Generalizing the model for stratified data analysis, including large strata, to compute stratum-specific risks and variances.

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Main Results:

  • An easily computable estimate of the joint population attributable risk and its asymptotic variance is obtained from the intercept.
  • Stratum-specific attributable risks and their variances can be calculated using stratum-specific intercept parameters.
  • A weighted sum of stratum-specific risks provides an estimate of the summary attributable risk and its variance when case sampling is independent of strata.

Conclusions:

  • The proposed logistic regression approach provides a computationally efficient way to estimate various forms of population attributable risk.
  • This method simplifies the analysis of both unmatched and stratified case-control data for public health and epidemiological research.