Adjusting for Publication Bias in Meta-Analysis with Continuous Outcomes: A Comparative Study

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. Three measures of continuous treatment effect, the mean difference, Cohen’s d and Hedges’ g, were considered. The methods studied were Copas, PET-PEESE, p-uniform, Trim and Fill and the limit meta-analysis. In addition, the performance of the random-effects meta-analysis using the DerSimonian estimator was also investigated. The analysis was conducted using a case study and an extensive simulation study including different scenarios. In general, 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.