Cumulative meta analysis: what works

Elena Kulinskaya, Eung Yaw Mah

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To present time-varying evidence, cumulative meta-analysis (CMA) updates results of previous meta-analyses to incorporate new study results. We investigate the properties of CMA, suggest possible improvements and provide the first in-depth simulation study of the use of CMA and CUSUM methods for detection of temporal trends in random-effects meta-analysis. We use the standardized mean difference (SMD) as an effect measure of interest. For CMA, we compare the standard inverse-variance-weighted estimation of the overall effect using REML-based estimation of between-study variance τ2 with the sample-size-weighted estimation of the effect accompanied by Kulinskaya-Dollinger-Bjørkestøl (Biometrics 2011; 67(1): 203–212) (KDB) estimation of τ2. For all methods, we consider Type 1 error under no shift and power under a shift in the mean in the random-effects model. To ameliorate the lack of power in CMA, we introduce two-stage CMA, in which τ2 is estimated at Stage 1 (from the first 5–10 studies), and further CMA monitors a target value of effect, keeping the τ2 value fixed. We recommend this two-stage CMA combined with cumulative testing for positive shift in τ2. In practice, use of CMA requires at least 15–20 studies.
Original languageEnglish
JournalResearch Synthesis Methods
Early online date24 Aug 2021
Publication statusE-pub ahead of print - 24 Aug 2021


  • CUSUM charts
  • effective-sample-size weights
  • inverse-variance weights
  • power
  • type 1 error

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