Cumulative meta analysis: What works

Elena Kulinskaya, Eung Yaw Mah

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Abstract

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
Pages (from-to)48-67
Number of pages20
JournalResearch Synthesis Methods
Volume13
Issue number1
Early online date24 Aug 2021
DOIs
Publication statusPublished - Jan 2022

Keywords

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

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