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Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

Jing Ning1, Yong Chen2, Jin Piao3

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Biostatistics (Oxford, England)
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Publication bias threatens meta-analysis. A new expectation-maximization (EM) algorithm effectively estimates publication bias using a Copas-like selection model, overcoming previous maximization challenges.

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Area of Science:

  • Biostatistics
  • Meta-analysis
  • Research methodology

Background:

  • Publication bias, where published results are unrepresentative, poses a significant threat to the validity of meta-analyses.
  • The Copas selection model offers a framework for addressing publication bias but presents computational challenges in maximizing the observed likelihood due to limited information on latent variables.

Purpose of the Study:

  • To propose an expectation-maximization (EM) algorithm for estimating parameters within a Copas-like selection model.
  • To address the computational difficulties in maximizing the observed likelihood associated with the Copas selection model.

Main Methods:

  • Developed and applied an expectation-maximization (EM) algorithm utilizing the full likelihood for parameter estimation.
  • Investigated a Copas-like selection model to analyze publication bias.

Main Results:

  • The proposed EM algorithm demonstrates robust performance in empirical simulation studies.
  • The EM algorithm successfully avoids the non-convergence issues encountered when maximizing the observed likelihood directly.
  • The associated inferential procedures derived from the EM algorithm are effective.

Conclusions:

  • The developed EM algorithm provides a reliable and computationally stable method for estimating publication bias under a Copas-like selection model.
  • This approach enhances the accuracy and trustworthiness of meta-analytic findings by effectively addressing publication bias.