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Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under

Wansu Chen1, Lei Qian2, Jiaxiao Shi2

  • 1Kaiser Permanente Southern California, Department of Research and Evaluation, 100 S. Los Robles Ave, 2nd Floor, Pasadena, CA, 91101, USA. Wansu.Chen@KP.org.

BMC Medical Research Methodology
|June 23, 2018
PubMed
Summary
This summary is machine-generated.

Robust Poisson regression models provide unbiased risk ratio estimates, unlike log-binomial models, when statistical assumptions are violated. This makes robust Poisson models preferable for accurate analysis in such scenarios.

Keywords:
Link function misspecificationLog-binomial regressionModel misspecificationRisk ratioRobust (modified) Poisson regression

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Log-binomial and robust Poisson regression are common for binary outcomes.
  • Previous research indicates similar performance under ideal conditions.
  • Model performance under misspecification remains unclear.

Purpose of the Study:

  • Compare log-binomial and robust Poisson models.
  • Evaluate performance under log link misspecification.
  • Assess behavior with non-linear predictor relationships.

Main Methods:

  • Simulation study design.
  • Comparison of statistical performance metrics.
  • Investigation of misspecified log link function.
  • Analysis of truncated response variable relationships.

Main Results:

  • Log-binomial estimates were biased with misspecification or right-tail truncation.
  • Bias increased with truncated observations and lower response rates.
  • Robust Poisson model estimates remained unbiased.

Conclusions:

  • Robust Poisson models are superior under model misspecification.
  • Robust Poisson models provide unbiased risk ratio estimates.
  • Preference for robust Poisson models in scenarios with violated assumptions.