Sequential change detection and monitoring of temporal trends in random-effects meta-analysis

Samson Henry Dogo, Allan Clark, Elena Kulinskaya

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
22 Downloads (Pure)


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 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 visualisation 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.
Original languageEnglish
Pages (from-to)220–235
Number of pages16
JournalResearch Synthesis Methods
Issue number2
Early online date8 Dec 2016
Publication statusPublished - Jun 2017


  • sequential meta-analysis
  • cumulative meta-analysis
  • bootstrap

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