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Related Experiment Videos

Maximum likelihood estimation of the attributable fraction from logistic models

S Greenland1, K Drescher

  • 1Department of Epidemiology, UCLA School of Public Health 90024-1772.

Biometrics
|September 1, 1993
PubMed
Summary

This study introduces maximum likelihood estimators for the attributable fraction in cohort and case-control studies. These new methods improve upon existing estimators, offering better performance in small samples.

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Biostatistics (Oxford, England)·2003

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Existing attributable fraction estimators for case-control studies, like Bruzzi et al. (1985), are not maximum likelihood estimators (MLEs).
  • These estimators utilize logistic models for relative risk but not for covariate distribution estimation.

Purpose of the Study:

  • To provide maximum likelihood estimators for the attributable fraction in both cohort and case-control studies.
  • To derive the asymptotic variances for these new estimators.
  • To generalize existing case-control attributable fraction estimators.

Main Methods:

  • Development of maximum likelihood estimators for attributable fraction in cohort and case-control study designs.
  • Derivation of asymptotic variances for the proposed estimators.
  • Comparison with existing methods, including generalization of Drescher and Schill (1991).

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Main Results:

  • The study presents novel maximum likelihood estimators for attributable fraction.
  • Asymptotic variances for these estimators are provided.
  • A simulation study indicates improved small-sample performance when confidence intervals are log-transformed.

Conclusions:

  • Maximum likelihood estimation provides a more robust approach for calculating attributable fractions.
  • The proposed estimators offer advancements for both cohort and case-control data analysis.
  • Log-transformed confidence intervals are recommended for better small-sample precision.