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

摘要

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 τ ². 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.