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Related Experiment Videos

Prevalence proportion ratios: estimation and hypothesis testing

T Skov1, J Deddens, M R Petersen

  • 1National Institute of Occupational Health, København O, Denmark.

International Journal of Epidemiology
|May 1, 1998
PubMed
Summary
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From the departing Editors in Chief.

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For cross-sectional studies, the log-binomial model is preferred over logistic regression and Cox regression. It provides accurate results without producing impossible prevalences greater than one.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Logistic regression may be inappropriate for analyzing cross-sectional studies of prevalent outcomes.
  • Alternative statistical methods have been proposed for such analyses.

Purpose of the Study:

  • To compare the performance of three methods for analyzing cross-sectional studies: log-binomial, GEE-logistic, and Cox regression models.
  • To evaluate their suitability for estimating prevalence and associated parameters.

Main Methods:

  • The study employed five sets of simulations under fourteen distinct conditions.
  • Performance was assessed based on bias, standard errors, and type I error probabilities.

Main Results:

  • All models showed negligible bias in parameter estimation.

Related Experiment Videos

  • Cox regression yielded inflated standard errors, particularly with high disease prevalence.
  • Log-binomial and GEE-logistic models maintained correct type I error rates.
  • GEE-logistic models could produce prevalences exceeding one, unlike log-binomial models.

    • Conclusions:

    • The log-binomial model is recommended for analyzing cross-sectional studies due to its accurate performance and ability to avoid impossible prevalence estimates.
    • It offers a reliable alternative to logistic regression and Cox regression in this context.