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Cumulative meta‐analysis: What works
Author(s) -
Kulinskaya Elena,
Mah Eung Yaw
Publication year - 2022
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1522
Subject(s) - cusum , statistics , meta analysis , random effects model , biometrics , variance (accounting) , sample size determination , restricted maximum likelihood , computer science , estimation , mathematics , econometrics , artificial intelligence , maximum likelihood , medicine , accounting , management , economics , business
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 τ 2 with the sample‐size‐weighted estimation of the effect accompanied by Kulinskaya–Dollinger–Bjørkestøl ( Biometrics . 2011; 67:203–212) (KDB) estimation of τ 2 . 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 τ 2 is estimated at Stage 1 (from the first 5–10 studies), and further CMA monitors a target value of effect, keeping the τ 2 value fixed. We recommend this two‐stage CMA combined with cumulative testing for positive shift in τ 2 . In practice, use of CMA requires at least 15–20 studies.