Adjusting for publication bias in meta-analysis with continuous outcomes: a comparative study

TL;DR

This study compares five methods for correcting publication bias in meta-analyses with continuous outcomes, finding PET-PEESE to be the most robust and least biased, despite some convergence issues.

Contribution

It provides a comprehensive comparison of five publication bias adjustment methods through case studies and simulations, highlighting PET-PEESE's robustness.

Findings

PET-PEESE is the most robust method.

Copas and PET-PEESE are least biased.

PET-PEESE handles heteroscedasticity well.

Abstract

Abstract Publication bias has been a problem facing meta-analysts. Methods adjusting for publication bias have been proposed in the literature. Comparative studies for methods adjusting for publication bias are found in the literature, but these studies are limited. We investigated and compared the performance of five methods adjusting for publication bias for the case of continuous outcomes. The methods studied are Copas, PET-PEESE, p-uniform, Trim & Fill and the limit meta-analysis. In addition, the performance of the random-effects meta-analysis using the DerSimonian estimator is also investigated. The analysis was done using a case-study and an extensive simulation study including different scenario's. The Copas and the PET-PEESE were found to be the least biased methods adjusting for publication bias. However, the Copas method, like other Likelihood-based methods, can have convergence issues. In addition, the PET-PEESE method is robust in case of heteroscedasticity, making the PET-PEESE method a preferable technique to adjust for publication bias.