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A Bayesian (meta-)regression model for treatment effects on the risk difference scale.

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Economics

Background:

  • Clinical decisions require understanding absolute risk reduction from treatments.
  • Logistic regression, common for binary outcomes, estimates effects as log odds differences.
  • Estimating treatment effects directly on the risk scale is clinically relevant.

Purpose of the Study:

  • To propose and evaluate a novel Bayesian meta-regression model for binary outcomes on the additive risk scale.
  • To compare this new model with existing methods (WTS model, logistic model back-transformation).
  • To assess model performance in network meta-analysis and simulated trials.

Main Methods:

  • Developed a Bayesian meta-regression model for binary outcomes on the additive risk scale.
  • Estimated treatment effects, covariate effects, and interactions directly on the linear risk scale.
  • Compared estimates with a Warn, Thompson, and Spiegelhalter (WTS) additive risk model and logistic model back-transformation.

Main Results:

  • Estimates diverged significantly between models, particularly with small sample sizes or risks near 0% or 100%.
  • The proposed model gave more weight to treatment effects in participants with extreme predicted risks.
  • The novel model's sensitivity was crucial for detecting all information in the hepatitis C network meta-analysis data.

Conclusions:

  • Researchers must recognize that modeling untransformed risk yields different results than standard logistic models.
  • The proposed Bayesian additive risk model provides a valuable alternative for estimating clinically relevant treatment effects.
  • This model is particularly useful in network meta-analysis settings with heterogeneous risk profiles.