title: Sequential change detection and monitoring of temporal trends in random-effects meta-analysis.
abstract: Temporal changes in magnitude of effect sizes reported in many areas of research are a threat to the credibility of the results and conclusions of meta-analysis. Numerous sequential methods for meta-analysis have been proposed to detect changes and monitor trends in effect sizes so that meta-analysis can be updated when necessary and interpreted based on the time it was conducted. The difficulties of sequential meta-analysis under the random-effects model are caused by dependencies in increments introduced by the estimation of the heterogeneity parameter τ2. In this paper, we propose the use of a retrospective cumulative sum (CUSUM)-type test with bootstrap critical values. This method allows retrospective analysis of the past trajectory of cumulative effects in random-effects meta-analysis and its visualization on a chart similar to CUSUM chart. Simulation results show that the new method demonstrates good control of Type I error regardless of the number or size of the studies and the amount of heterogeneity. Application of the new method is illustrated on two examples of medical meta-analyses. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.
authors.affiliation: School of Computing Sciences, University of East Anglia, Norwich, UK.
authors.forename: Samson Henry
authors.initials: SH
authors.lastname: Dogo
authors.affiliation: School of Computing Sciences, University of East Anglia, Norwich, UK.
authors.forename: Allan
authors.initials: A
authors.lastname: Clark
authors.affiliation: School of Computing Sciences, University of East Anglia, Norwich, UK.
authors.forename: Elena
authors.initials: E
authors.lastname: Kulinskaya
mesh_terms: D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D012107:Research Design
keywords: CUSUM; bootstrap; cumulative meta-analysis; sequential meta-analysis