title: Identifying null meta-analyses that are ripe for updating.
abstract: BACKGROUND
  As an increasingly large number of meta-analyses are published, quantitative methods are needed to help clinicians and systematic review teams determine when meta-analyses are not up to date.
  
  
  METHODS
  We propose new methods for determining when non-significant meta-analytic results might be overturned, based on a prediction of the number of participants required in new studies. To guide decision making, we introduce the "new participant ratio", the ratio of the actual number of participants in new studies to the predicted number required to obtain statistical significance. A simulation study was conducted to study the performance of our methods and a real meta-analysis provides further evidence.
  
  
  RESULTS
  In our three simulation configurations, our diagnostic test for determining whether a meta-analysis is out of date had sensitivity of 55%, 62%, and 49% with corresponding specificity of 85%, 80%, and 90% respectively.
  
  
  CONCLUSIONS
  Simulations suggest that our methods are able to detect out-of-date meta-analyses. These quick and approximate methods show promise for use by systematic review teams to help decide whether to commit the considerable resources required to update a meta-analysis. Further investigation and evaluation of the methods is required before they can be recommended for general use.
authors.affiliation: Chalmers Research Group, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada. nbarrowman@cheo.on.ca
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Barrowman
authors.forename: Manchun
authors.initials: M
authors.lastname: Fang
authors.forename: Margaret
authors.initials: M
authors.lastname: Sampson
authors.forename: David
authors.initials: D
authors.lastname: Moher
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D015201:Meta-Analysis as Topic; D009203:Myocardial Infarction / Q000188:drug therapy; D012372:ROC Curve; D018401:Sample Size; D013300:Streptokinase / Q000627:therapeutic use
chemical_list: D013300:Streptokinase