Modelling publication bias in meta-analysis: a review

Abstract

Meta-analysis is now a widely used technique for summarizing evidence from multiple studies. Publication bias, the bias induced by the fact that research with statistically significant results is potentially more likely to be submitted and published than work with null or non-significant results, poses a thereat to the validity of such analyses. The implication of this is that combining only the identified published studies uncritically may lead to an incorrect, usually over optimistic, conclusion. How publication bias should be addressed when carrying out a meta-analysis is currently a hotly debated subject. While statistical methods to test for its presence are starting be used, they do not address the problem of how to proceed if publication bias is suspected. This paper provides a review of methods, which can be employed as a sensitivity analysis to assess the likely impact of publication bias on a meta-analysis. It is hoped that this will raise awareness of such methods, and promote their use and development, as well as provide an agenda for future research.