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Standardized binomial models for risk or prevalence ratios and differences.

David B Richardson1, Alan C Kinlaw2, Richard F MacLehose3

  • 1Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA and david.richardson@unc.edu.

International Journal of Epidemiology
|August 1, 2015
PubMed
Summary
This summary is machine-generated.

Epidemiologists can now estimate risk or prevalence ratios and differences using a marginal structural binomial regression model. This method improves upon logistic regression for common outcomes and offers population-level exposure effect estimates.

Keywords:
Riskprevalenceregression modelsstandardizations

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Logistic regression models yield odds ratios for binary outcomes in epidemiological studies.
  • Odds ratios approximate risk ratios for rare outcomes but not common ones.
  • Log binomial regression is an alternative for directly estimating risk or prevalence ratios.

Purpose of the Study:

  • To describe and illustrate a marginal structural binomial regression model for estimating standardized risk or prevalence ratios and differences.
  • To address limitations of traditional logistic and log binomial regression models in epidemiological analysis.

Main Methods:

  • Utilized a marginal structural binomial regression model.
  • Applied the approach to a cohort study of coronary heart disease in Evans County, Georgia.
  • Shifted explanatory variables to standardization weights to improve model convergence.

Main Results:

  • The proposed model effectively estimates standardized risk or prevalence ratios and differences.
  • Addressed common model convergence issues associated with log binomial regression.
  • Facilitated the evaluation of additive effects of multiple exposures.

Conclusions:

  • Epidemiologists should consider reporting standardized risk or prevalence ratios and differences in cohort and cross-sectional studies.
  • These measures are accessible using statistical software like SAS, Stata, and R.
  • The marginal structural model estimates exposure effects at the total population level.