title: Meta-analysis for evidence synthesis in plant pathology: an overview.
abstract: Meta-analysis is the analysis of the results of multiple studies, which is typically performed in order to synthesize evidence from many possible sources in a formal probabilistic manner. In a simple sense, the outcome of each study becomes a single observation in the meta-analysis of all available studies. The methodology was developed originally in the social sciences by Smith, Glass, Rosenthal, Hunter, and Schmidt, based on earlier pioneering contributions in statistics by Fisher, Pearson, Yates, and Cochran, but this approach to research synthesis has now been embraced within many scientific disciplines. However, only a handful of articles have been published in plant pathology and related fields utilizing meta-analysis. After reviewing basic concepts and approaches, methods for estimating parameters and interpreting results are shown. The advantages of meta-analysis are presented in terms of prediction and risk analysis, and the high statistical power that can be achieved for detecting significant effects of treatments or significant relationships between variables. Based on power considerations, the fallacy of naïve counting of P values in a narrative review is demonstrated. Although there are many advantages to meta-analysis, results can be biased if the analysis is based on a nonrepresentative sample of study outcomes. Therefore, novel approaches for characterizing the upper bound on the bias are discussed, in order to show the robustness of meta-analysis to possible violation of assumptions.
authors.affiliation: Department of Plant Pathology, Ohio State University, Ohio Agricultural Research and Development Center, Wooster 44691, USA. madden.1@osu.edu
authors.forename: L V
authors.initials: LV
authors.lastname: Madden
authors.forename: P A
authors.initials: PA
authors.lastname: Paul
mesh_terms: D003627:Data Interpretation, Statistical; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D008962:Models, Theoretical; D010935:Plant Diseases / Q000706:statistics & numerical data*; D010944:Plants / Q000382:microbiology*; D017594:Publication Bias; D012107:Research Design