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Estimation of adjusted rate differences using additive negative binomial regression.

Mark W Donoghoe1,2, Ian C Marschner1,2

  • 1Department of Statistics, Macquarie University, Sydney, 2109, New South Wales, Australia.

Statistics in Medicine
|April 14, 2016
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Summary
This summary is machine-generated.

This study introduces a new method for fitting additive negative binomial regression models, addressing overdispersion in event count data. The approach reliably estimates rate differences, offering a valuable tool for biostatistical analysis.

Keywords:
ECME algorithmnegative binomial regressionoverdispersionrate differencesemi-parametric regression

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Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Rate differences are crucial effect measures in biostatistics, offering an alternative to rate ratios.
  • Standard additive Poisson regression models struggle with overdispersed event count data, where mean and variance are unequal.

Purpose of the Study:

  • To propose a novel, reliable method for fitting additive negative binomial regression models.
  • To address limitations in standard model-fitting methods for constrained parameter spaces in additive models.

Main Methods:

  • Utilized a variant of the expectation-conditional maximization-either algorithm.
  • Developed a method to reliably fit additive negative binomial regression models.
  • Enabled flexible generalizations using semi-parametric regression functions.

Main Results:

  • The proposed method reliably fits additive negative binomial regression models.
  • The approach successfully handles overdispersed event count data.
  • Demonstrated application in a clinical trial for diabetic retinopathy treatment.

Conclusions:

  • The novel expectation-conditional maximization-either algorithm variant provides a robust solution for additive negative binomial regression.
  • This method enhances the analysis of event count data with overdispersion.
  • An R package is available for implementing the proposed statistical method.