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On modelling relative risks for longitudinal binomial responses: implications from two dueling paradigms.

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Summary
This summary is machine-generated.

This study extends modified Poisson regression for relative risk (RR) analysis to longitudinal binary data. A generalized estimating equation (GEE) approach is developed, offering a coherent method for RR inference in longitudinal studies.

Keywords:
ORgeneralized estimating equationsrelative risksandwich variance estimatorsemiparametric generalized linear models

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Logistic regression is common for binary outcomes, but relative risk (RR) is often preferred for interpretability.
  • Zou's modified Poisson regression effectively models RR for cross-sectional data, gaining significant traction in research.
  • There is a need to extend RR modeling to longitudinal data, commonly analyzed using generalized linear mixed-effects models (GLMM) or generalized estimating equations (GEE).

Purpose of the Study:

  • To develop a longitudinal regression model for binary responses that allows for relative risk (RR) inference.
  • To extend Zou's modified Poisson regression approach to longitudinal data settings.
  • To provide a statistically sound method for estimating relative risk in longitudinal studies.

Main Methods:

  • The study proposes a generalized estimating equation (GEE) based longitudinal model.
  • The GEE approach is chosen for its semiparametric nature, aligning with the assumptions of modified Poisson regression.
  • This method avoids the parametric distributional assumptions inherent in generalized linear mixed-effects models (GLMM).

Main Results:

  • A GEE-based longitudinal model is successfully developed for binary responses.
  • The proposed model provides a coherent framework for estimating relative risk (RR) in longitudinal studies.
  • This approach is compatible with Zou's modified Poisson regression for cross-sectional data.

Conclusions:

  • The developed GEE-based longitudinal model effectively extends relative risk (RR) analysis to longitudinal binary data.
  • This method offers a valuable alternative to traditional logistic regression when RR is the preferred measure of effect.
  • The approach provides a robust framework for epidemiological and clinical research involving longitudinal binary outcomes.