LAUSR

Meta-analyses and systematic reviews are highly valued as evidence for clinical decisionand policy-making. However, inference in these settings may be invalid if the studies do not come from the same underlying distribution. Small study effects is one form of heterogeneity that can lead to biased estimates, particularly if it arises due to the selective publishing of studies, a phenomenon known as publication bias. In this paper we discuss landmark methods for diagnosing the presence of small-study effects and correcting for them, as well as the limitations of each method. We also identify ongoing challenges and key areas in need of methodological innovation.